Artificial Intelligence

Explainable artificial intelligence in healthcare by Dr. Johnson Thomas, MD, FACE, @JohnsonThomasMD

Debates are raging on social media regarding explainable AI in healthcare. Geoffrey Hinton, one of the ‘godfathers of AI’ recently tweeted – “Suppose you have cancer and you have to choose between a black box AI surgeon that cannot explain how it works but has a 90% cure rate and a human surgeon with an 80% cure rate.

Healthcare Decoded – The #Analytics Conundrum by Harish Rijhwani, @Harish_Rijhwani

If we want to start using Analytics in India, one of the areas to focus on can be in the area of Diagnostic Analytics. We can leverage Transfer learning in this area as there are many pre-trained models leveraged by others and available. 

Algorithms in #EMR by Dr. Joyoti Goswami @Joyoti10

Practicing physicians these days are barraged with a lot of technical jargon promoted by the Information technology professionals such as Big Data, Hadoop, Artificial Intelligence and Predictive analytics. For a physician not introduced to the these terms, the conversation is of little value unless there is a specified value in the clinical setting.

What does it take to build real-world #AI enabled healthcare solution? By Vijayananda J, @vijayanandaj


Development of new technologies has undoubtedly enabled several breakthroughs in the healthcare industry. To put it simply, it has revolutionised the growth of healthcare from nascent patient-care to accomplishing treatment of life-threatening diseases. High-performance computing and the availability of digital data have extended these remarkable outcomes explaining why AI-based healthcare solutions are at top of the funding lists and are continuously gaining traction.


The progress in research due to AI is facilitating Machine Learning (ML) techniques with potential to reduce costs, improve results, and increase patient satisfaction. However, applying ML techniques to create AI models is only a small portion in the overall realization of a clinical solution. Let me illustrate this with an example. Consider two cases – identifying a person from your personal photos vs. identifying a lesion in MR images of the brain. Both of these can be implemented using standard AI techniques but there would be a considerable variance in how quickly they could be deployed in use by the end-user.

So, what does it take to build a real-world AI-enabled clinical solution? First, let me draw attention to the principle challenges that exist today – the acquisition and aggregation of data. In the world of AI, it is often believed that one who has access to data is the king. But, in my view, one who has access to curated, annotated and consent managed data is the emperor of the world!

Unlike the large collections of public datasets that are available for creating general-purpose applications such as ImageNet, CIFAR etc., there is a significant shortage of high-quality medical datasets of specific clinical conditions. A vast majority of medical data is unstructured and dispersed across various systems, which makes data acquisition, extremely complex. Furthermore, region specific data residency and privacy laws also limit the sharing of patient data.

Once the problem of data acquisition is resolved, we are then confronted with the issues of quality and diversity. For example, to develop a lung-abnormality detection application, thousands of CXR images for each of the abnormalities – both in isolation and combination are required. Searching the hospital image archive for specific abnormalities in CXR images is highly error prone due to amount of manual effort required. In addition, for many clinical problems, a single data point is inadequate and needs more elements from the longitudinal record of the patient such as imaging priors, radiology reports, genomic data, lab reports etc.

Before the model creation, the data scientist has to spend a considerable amount of time and effort on the pre-processing steps. For example, to test the condition of the cells in Pathology, pre-processing steps like detection and segmentation of nuclei and glands are required, which themselves demand a highly accurate AI model. Although Deep Learning (DL) models are good at automatically extracting the features, a number of traditional features (like pathomics in pathology) improve the overall accuracy of a classifier. The challenging part is of course to find out which features are relevant and influential on the final prediction model.

Here comes the final and most appealing part of AI – the model development. It is only through close consultation with clinical experts that a data scientist can understand the problem statement for building the AI model. The more adept the data scientist is at choosing the analytical method, the higher will be accuracy of the model. Additionally, choosing a ML vs. DL technique holds significant importance in healthcare since the solution needs to justify the end result.

At Philips, we think of AI-enabled solutions as an extension of human capabilities, rather than a replacement. Any AI-enabled solution needs to demonstrate that it can augment the healthcare provider’s expertise in a meaningful and accurate manner. As the model is trained on a limited dataset, it is critical to be aware of its limitations when tested on datasets from diverse regions.


Despite the progress on the interoperability front, there are very few common representations of clinical pathways in healthcare. An AI model can’t be implemented in a hospital using the same parameters that were used to design it in the lab. Thus, it’s imperative to integrate it seamlessly with the existing clinical workflow of hospitals.

To ease the deployment of AI-enabled solutions in healthcare while keeping the aspects of hosting, performance and security in mind, we decided to build the HealthSuite Insights in Philips based on inputs from the data science and clinical communities.

Here are a few recommendations from us, which can simplify the overall process of creating AI-enabled clinical solutions. Firstly, churn out a cross-functional team of data scientists, clinicians, product managers, application specialists, software engineers, user-experience experts, DevOps and Q-and-R engineers. Then, study the clinical problem statement and role out the limitations of the existing solution. Further, establish clarity on the intended use of the proposed solution and clinical workflow integration. Institute a comprehensive data management strategy including acquisition and curation and acquire data from geographically distributed institutions.

Spend quality time in real-time testing to determine the overall accuracy of the model and focus on matters of privacy including consent management, security and regulatory. To eliminate issues pertaining to scalability, adopt an ML platform that can accelerate the development and deployment phases. Therefore, a comprehensive validation strategy that considers the intended use of the solution, interpretation variabilities, data and workflow diversity can help us confront the challenges and unlock the immense potential of AI in healthcare.

AI is an emerging technology. The healthcare institutions which will invest in open and interoperable standards for data management and clinical workflows today, will pioneer the implementation of AI-enabled solutions tomorrow. This will not only lead to better outcomes for patients but also significantly simplify the overall healthcare system.

In conclusion, I would like to quote an ancient Indian Sanskrit verse that keeps me personally motivated as we undertake this revolutionary journey: 

The article was first published on the Author’s LinkedIn pulse blog, its republished here with the Author’s permission. 
Author
Vijayananda J

Chief Architect and Fellow, at Philips Healthcare. Lead Architect and responsible for driving the strategy, architecture and technology roadmap for the Big Data, Analytics and Machine Learning platform within Philips HealthTech

A Data Scientist’s Experience in Decoding Chest Imaging by Vidya MS


The Chest Imaging Update 2018 held by the Narayana Health group, brought together over 150 radiologists, pulmonologists and doctors gathered to update and improve their knowledge in the reporting of Chest Imaging, both X-ray and CT. As a data scientist with keen interest in medical imaging, my aim was to get an inside look into the daily practice of medical professionals in detection and diagnosis of pulmonary diseases.


The conference opened with Dr. Vimal Raj, Conference chair and an accomplished Cardiothoracic Radiologist, stating that thoracic imaging is not reported well enough and there is a lot more value that a radiologist could add in the diagnosis to enable better treatment options for the patient. The flow of the subsequent sessions was extremely well constructed reflecting the everyday reporting workflow in the chest imaging space – starting from assessment of the humble chest X-ray to the CT followed by diagnosis and treatment plans. The second day was mainly focused on hands on sessions.
The first session of the day was conducted by Dr. Kishore Kumar from NH and Dr. Aparna Irodi from CMC Vellore on what never to miss on a chest X-ray and assessment of neonatal chest-X-rays. Dr. Kishore covered the important parts to access in a chest X-ray before reporting it as a normal one. One of the challenges that he brought out was that these areas are often easily missed and there is large amount of misdiagnosis made while assessing a chest X-ray which could led to significant impact later on for the patient. One case was presented where an early stage opacity, which could have been easily caught, progressed into a cancerous stage. There are a multitude of reasons for misdiagnosis, including, superimposition of thoracic structures, similarity in radiographic appearances of some chest diseases, and subtlety of some chest pathologies rendering them indistinguishable. Each chest X-ray takes a trained radiologist several minutes to review and given the ubiquitous nature of the X-ray in the imaging world, this often leads to significant increase in the workload. Another reason could be the variance of reporting across radiologists due to inconsistent terminologies.
The sessions then shifted towards the more complex CT imaging, where the physics and protocols behind the CT imaging were first introduced, followed by multiple sessions on reporting of lung cancer on a CT and standard definitions of chest patterns on CXR and CT to ensure better reporting.
Since the conference was focused on updating radiologists and how to better report their studies, the important question on why accurate reporting is needed was excellently handled by Dr. Murali Mohan. He walked through a comprehensive series of statistics conducted by various institutions, to explain why the importance of reporting accurately.
One of the highlights and most interesting sessions for the day for me, was the ‘Multi-Disciplinary Team’ panel featuring, Dr. Vimal Raj, Dr. Murali, Dr. Aparna, Dr. Rajani Bhat, Dr. Ranganatha R. They wonderfully presented how an MDT team conclude in diagnosis and determining a course of action when things are unclear. This was extremely insightful for an outsider like myself to truly understand the complications, uncertainties and how the various disciplines come together to define the best course of action for the patient.
Considering all the complications mentioned above, the variations in manifestations of the same medical condition across demographics, overlap of multiple patterns and mimic cases, many of these cases require significant expertise in the diagnosis of a thoracic scan. There is also a high variance in reporting across radiologists and the opinions are sometimes subjective especially in chest X-rays. Many of these uncertainties sometimes require a group of expertise to reach a consensus. In addition to these clinical challenges, developing an AI system to aid the reporting process requires large amounts of consistently reported data to truly learn the patterns on an image. Many of these systems also do not have access to much of the other patient information that may be pivotal in assessment. The difficulty in obtaining such large high-quality data makes it more complex to build these systems.
AI is still not there to diagnose the myriad of pathologies on a thoracic scan, primarily the chest X-ray though there have been many solutions claiming to detect a limited set of abnormalities. That being said, with the immense improvement in technology in the recent years, AI can certainly aid a radiologist in diagnosis and subsequently the treatment. The focus for AI models should be around aiding radiologists in repetitive tasks, ensuring that highly skilled expertise focuses on more complex and abnormal cases, detecting and highlighting unusual patterns in the image (especially those than can be easily missed), and also in providing clinical insights to a radiologist in the detection of abnormal regions.

Finally, I would like to thank the organizers of the Chest Imaging Update 2018. Overall, it was a wonderful experience for me to be in the midst of a large group of experts from various disciplines, interact with some of them to understand the key problem areas. 

The article was first published on the Author’s LinkedIn pulse Blog, its been re-published here with the Author’s permission. All ideas presented are author’s personal views

Author
Vidya S.M

Data Scientist at Philips. Data Scientist with a demonstrated history of working in the hospital & health care industry. Skilled in AI, image processing, & algorithms. Strong engineering professional with a Master’s degree focused in Machine Learning & AI from National University of Singapore.

I & L to #AI & #ML in Healthcare by Jyoti Sahai, @jyotisahai

Have you ever wondered why if confronted with any illness symptoms that appear even a bit abnormal, we prefer to consult with a doctor in a large hospital only, even though a more competent doctor may have a clinic next door itself.

And have you ever wondered that what that preference has to do with Artificial Intelligence (AI) and Machine Learning (ML)!
To explain that, let me recount what happened to me twenty-one years back. I vividly remember that incident from 1997 that I can now relate well to the significance AI and ML are having in healthcare currently!

SYMPTOM

I was being examined by a leading physician at Agra (who had an experience of over twenty-five years and had a roaring practice) for a pain near my left toe. The conversation progressed as follows:
I: Doc Sahib, please have a look at my left toe. I am troubled by a severe pain for over last three weeks. I cannot put my foot down or wear the shoes even.
Doctor: Did you ever have this type of pain before?
I: No
Doctor: (Examining the pain area closely) Do you feel any irritation, or feel any urge to scratch that area?
I: No.
Doctor: Do you eat lot of red meat?
I: No! I am a vegetarian.
Doctor: Do you like to eat lot of tomatoes, or cheese, or spinach or any other high protein foods.
I: Yes. Very frequently have cheese-spread, and baked beans in breakfast, and of course tomato in some form is generally there in all meals.
Doctor: (Prepares a slip for the diagnostic lab) Please have the uric acid blood test done as I suspect you have gout.
I: Thanks, Doctor. Will come back later with the test results.
(Later during the day)
I: (Handing over the lab report) Here Doc Sahib. Please have a look at the report.
Doctor: (Going through the test report) That is what I thought. You have gout! Your uric acid level is 12.4 mg/dl which ideally should have been between 3.5 mg/dl – 7.0 mg/dl. I will immediately start the medication.
(The doctor then spent few minutes to explain what gout was and how it impacted my health, and my lifestyle.)
I: Any restrictions on diet?
Doctor: Yes. For the time being completely stop eating your favorites – cheese, tomato, spinach and all dals (lintels) except ‘moong’ dal.
The treatment started that same day, and within three weeks the pain had substantially subsided, and gout was well under control.
What I have narrated above was actually the Step 3 of the treatment plan that I had followed for almost three weeks before I met that doctor at Agra.
  • The symptoms – After having spent more than five years in Bangalore I had just moved to Noida and had started to adjust to a different living (and professional) environment. One day I woke up to acute pain in the area near my left toe. It appeared a little swollen and made it difficult for me to even wear the shoes.
  • Treatment Step 0 – As usually happens with all of us, initially I tried out some home remedies only, like soaking the leg in warm water and taking some pain killers. That was to no avail and the pain persisted.
  • Treatment Step 1 – A few days later I had to attend a family gathering where a relative of mine, fresh out of college after completing her course in medicine, had a look at it and opined that it could be some allergic reaction due to change of location (from Bangalore to Noida) and prescribed some tablets. However, the pain still persisted and even increased after few days of that treatment.
  • Treatment Step 2 – It was then that I decided to consult a practicing physician and went to a clinic just across the road where we lived. He examined the pain area and diagnosed it as some sort of inflammation and advised putting poultice for few days. Even after several days of that treatment, the pain did not subside but actually aggravated.
  • Treatment Step 3 – Experiencing no relief for over three weeks, I finally decided to consult my younger brother, a leading plastic surgeon at Agra, who took me to one of his colleagues who was a leading physician. What happened next, I have already stated above.

DIAGNOSIS

Now after twenty-one years when I analyze that line of treatment, I realize that
  1. The young doctor who first advised me maybe had never seen such symptoms earlier and thus was not able to diagnose correctly.
  2. The physician I consulted next might have seen only a few patients with a similar set of symptoms that I had (but not with the same illness), therefore was not able to formulate the right questions to ask that could have led to the correct diagnosis from a set of possible outcomes arising from similar symptoms.
  3. However, the doctor at Agra with his vast experience, had obviously seen those set of symptoms several times earlier and had acquired sufficient I and L to treat such cases effectively.
  4. Thus, though all the three doctors were surely competent, what the first two doctors obviously lacked were
  • the ability to apply their I (Intelligence) in (a) arriving at the correct diagnosis based on the symptoms they were presented with, and (b) subsequently determining an appropriate line of treatment; and
  • the extent of L (Learning) that comes with experience of treating hundreds and thousands of patients with various types of symptoms possible that brings in the knowledge that what could be the possible diagnoses and what treatments worked or did not, and why or why not?

LINE OF TREATMENT

By applying AI and ML techniques and solutions in healthcare it may now become possible to make available the accumulated I and L – resulting from the large number of successful (and unsuccessful) treatments by various experienced doctors – to those competent but less experienced physicians.
With access to an appropriate AI/ML system, even a physician in a small clinic in a remote location could
  • draw upon the accumulated experience of other successful doctors;
  • be guided properly to arrive at the correct diagnosis and subsequently to determine an appropriate line of treatment; and
  • confirm that the planned line of treatment is suitable for the medical profile of the patient. In case the patient’s medical profile is not readily available (like in case of emergency patients or admittances to trauma centers), AI/ML systems could caution the first medical responders on the possible complications (if any) associated with any planned line of treatment.

OUTCOME

Effective use of AI/ML systems in healthcare can deliver sustained benefits for all relevant stakeholders:
For the patient:
  • Assurance that the physician would arrive at a correct diagnosis, and would propose an appropriate and effective line of treatment with less or almost no margin of error;
  • Obviating the need to rush to a larger hospital/clinic just because the symptoms are a bit abnormal; and
  • Faster and more effective response from medics in emergency cases.
For the healthcare provider:
  • Increased efficiency with lower turnaround time for patients;
  • Faster and accurate diagnosis and effective treatment;
  • Substantial reduction in unfair treatment cases; and
  • Substantially faster and accurate response by first medical responders in emergency cases.

FOLLOW-UP

In the face of massive disruption taking place in healthcare space, and the frantic pace of medical data generation, any AI/ML system is likely to be soon become outdated, ineffective and irrelevant, if it is not constantly updating its Intelligence and is not constantly Learning.
Thus, it is imperative that all instances of successes and failures, arising out of using any AI/ML system, are fed back into that system to ensure constant refinement of its algorithms. That will result in it providing even more accurate outcomes for future users.

Conclusion

From the above it is evident that an AI/ML system can be a powerful ally of a physician and its deployment should not be termed as “man against machine” by any means.
In my opinion, AI/ML technologies are still meant to assist the medical fraternity and are not really likely to replace doctors (at least in foreseeable future)!
The article has been republished here with the authors permission. The article was first published in the authors’ linkedin pulse page.

Author
Jyoti Sahai

Chairman and Managing Director at Kavaii Business Analytics India Pvt. Ltd. Jyoti Sahai has over 42 years of experience in banking and IT industry, and is currently the CMD of Kavaii Business Analytics India. Kavaii provides analytic solutions in Healthcare and IT Services domains.

Artificial Intelligence #AI can help address current healthcare challenges in India, Dr Sandeep Reddy @docsunny50

Earlier this year, while making a keynote speech at an Artificial Intelligence (AI) in Health conference in Dubai, I mentioned that AI techniques can be used to address some of the intractable health issues in developing countries. 


This comment was picked up by a journalist of an online news site and reported as the headline of a news item covering the conference. However, the journalist also mentioned I hadn’t provided any details to qualify my comments. To my defence, the focus of my speech was not about this topic. Subsequently, in a conversation a week ago with the founder of an US-based AI health start-up, we discussed the multitude of opportunities in using AI enabled health services in developing countries and how few are aware of these opportunities. These circumstances and a reminder from the editor of this site about my promise to contribute an article has now led me to articulate the benefits of AI in health in the context of developing countries. Here, I use India as a typical developing country but many of the processes I discuss can potentially be used in other developing countries.


First, let us discuss some of the common issues that health services in developing countries face. A common grievance of health services is the lack of qualified workforce to treat and manage patients. Where health services have qualified personnel, they are overloaded with patients affecting the quality of the service they provide. The other common issue is the urban-rural maldistribution of qualified physicians. The preference of physicians to practice in urban health centres has led to a skewed distribution favouring urban centres and disadvantaging rural communities. In spite of government initiatives to push quality rural health services, the urban-rural divide is to stay. Another prominent issue is the variability in the quality of health services provided in different parts of the country and sometimes within the same region or city. This inconsistency is because of poor monitoring of health services by national accreditation bodies or poor compliance with quality standards by health services. Further, outbreaks of infectious diseases because of mainly environmental reasons has become alarmingly frequent in developing countries. Poor surveillance infrastructure means these outbreaks can progress to epidemics in a span of days. These aren’t the only health system concerns in developing countries but are areas that I think will benefit from the application of AI techniques.


India with its massive population of 1.35 billion (2018 population estimate) is in dire need of strong health infrastructure and government policy to service the nation’s health needs. India, just like many developing countries, has significant challenges in delivering this requirement. A combination of increasing burden from chronic diseases, a large ageing population, qualified personnel shortage, urban-rural divide, low government investment in health, inadequate health insurance coverage and variable quality of health service delivery have contributed to this state of affairs. However, the Government of India has been lately active in firming up the health policy and strengthening the health infrastructure. One of the major initiatives of the government was the release of the National Health Policy last year with an aim to reinvigorate the healthcare delivery in India by increasing health spending, establishing national quality standards, promoting evidence-based healthcare and introduction of digital health initiatives. With regards to the latter objective, the intention to set up a National Digital Health Authority and promote interoperable Electronic Health Record systems across India will create a strong foundation for digital health innovations to be applied. This digital platform will also provide opportunities for Foreign Direct Investment (FDI) and contribute to further growth of digital health in India. As digital health initiatives ramp up in India, opportunities for application of AI will also open up.

So how would AI applications help the Indian health system? Earlier, I discussed the healthcare delivery challenges developing countries face. The same difficulties apply to India too. AI systems driven by deep neural networks and computer vision have matched accuracy levels of human clinicians in interpreting radiological, fundoscopic and histopathological images. Intelligent agents are also being used to mine data and analyse electronic health records to assist clinicians in the medical diagnosis and predicting mortality of patients. Also, machine learning and natural language processing driven mobile applications are being used to communicate with patients and aid medication adherence, healthy lifestyles and schedule visits to doctors. Further, AI applications are being used in hospitals to predict the length of stay of patients and formulate treatment plans for them. All of these developments are detailed in several academic journals and the media. Application of these agents will have a profound effect on the Indian healthcare landscape, where a shortage of qualified specialists and diagnostic centres abound. While the AI systems may not be able to replicate all the capabilities of the medical specialists, it will in combination with telemedicine approaches be able to increase healthcare access for underserved communities and alleviate the burden of overstretched health services.


AI systems can also aid in the improvement of the quality of healthcare by reducing the variability of healthcare delivery and enabling evidence-based practice across the country. By incorporating government sanctioned and thoroughly evaluated AI applications in healthcare delivery, standardisation of healthcare delivery can be achieved. With inconsistency in healthcare delivery and non-evidence-based practices being common in India, roll out of authorised clinical decision support systems that run through machine learning processes will contribute to standardisation of healthcare delivery. Also, AI systems through ongoing analysis of ecological, biogeographical and public health data can alert authorities about outbreaks of infectious diseases and help contain the spread. For example, in recent years machine learning has been used to identify sources of outbreaks. During the Ebola outbreak in Africa, machine learning was used to analyse ecological data to determine the bat species harbouring Ebola virus and contain the spread of the disease. Thus, AI agents can also be used to strengthen India’s communicable disease surveillance infrastructure.

While the use of AI applications presents significant promise for the Indian healthcare system, one has also to be cognizant of the challenges in applying AI approaches. AI applications rely on a robust digital health foundation including ongoing access to electronic patient data and patient/population information management systems. With the Indian digital health infrastructure being nascent at best, widespread roll-out of AI applications can be a challenge. Also, with the low number of qualified health informaticians, machine learning trained data scientists and AI focused entities in India, there may be increased reliance on overseas companies to support the roll-out of AI applications. There are also issues like bias, lack of contextual reasoning and explainability problems that accompany AI applications. However, with advances in AI technology some these issues have now been addressed with number of solutions available.


To harness the benefit of AI approaches, the Indian government has to formulate a definitive AI strategy. A strategy that amongst many other things outlines the regulatory framework and implementation strategy for the roll-out of AI in India. The immense benefits that come through application of AI can be only be realised through the boldness and proactiveness of the Indian government. By pushing forward a national AI strategy and setting up an AI enabled healthcare delivery system, India can be a leading example for other countries as to how critical healthcare challenges can be addressed through AI approaches.

Author
Dr Sandeep Reddy, MBBS DPH MSc MMgmt MBAcert PhD CHIA

I am a Certified Medical Informatician, Health Program Evaluator and Artificial Intelligence in Medicine (AIM) Researcher with education, training and experience from leading institutions and various parts of the world. I am currently focused on the research and application of artificial intelligence techniques and program evaluation methodologies in the healthcare sector. In addition to peer reviewed and non-peer reviewed articles, I have authored two books and in the process of completing another one. More about me can be found here: http://www.drsandeepreddy.com

4 hints to get started with #AI in your company by Devesh Rajadhyax @deveshrajadhyax

Most companies are working on Digital Transformation today, and Artificial Intelligence is a critical part of that transformation.

Two questions immediately present themselves-
1.    What is Digital Transformation and how it is different from the IT/ICT transformation that is happening since for than four decades?
2.    Why is AI a critical part of this transformation?

Let me take the first question.

Digital Transformation is actually a cognitive revolution. It is a more humanlike way of making sense of the world around. And this is our clue to the most important difference between IT and Digital-
IT systems are not humanlike. They don’t try to make sense of the world around them. They create a small world of their own and everyone follows the rules of that world. The input has to be given in the way they demand and output will be available in forms that they prefer. You better fall in line.
Digital, or Cognitive systems would want to fit into the world they find themselves. To understand this, see how we humans manage our operations. We take in all the signals from the real world. We see, hear, feel, smell and then speak, show, move and push at the world around us. That’s what Digital systems aim to do. They want to process all the available inputs and then interact in a natural way. That’s why they hope to solve problems much closer to our life.’
Now let’s turn to the second question, why AI is so critical in this transformation.
I think the above distinction must have given you an intuitive answer. Because we are aiming for a humanlike way of interacting with the world, we need AI to do the processing. We also need sensors (IoT) and motors (Robotics), displays (VR) and so on. You can see how well the various Digital Technologies fit in this paradigm.
But even before AI, there is a big big factor that drives this whole transformation, and that factor is Data.
Since we are talking about real world data, we are talking huge huge volumes in forms that we never processed before. Vision, audio, waveforms, text, handwriting and many more types of data need to be captured (again IoT), stored and processed (Cloud, Big Data) and managed (Blockchain). But if you are the one responsible for planning and implementing systems, this central importance of data means a fundamental change for you.
When you plan classical IT systems, you start with the objectives of the company. Accordingly, you define the requirements. Analysis and design are further carried out on the requirements. Data is an outcome of this process.
Fig 1: Planning for classical IT systems
In Digital systems, however, data drives the system planning. This is again somewhat like like humans. We cannot demand more data from the world. We work out our affairs in such a way that we can manage with whatever data the world gives us. And we have become very good at extracting as much meaning from that data as possible.
So the new paradigm will be:
Fig 2: Planning for Digital systems
As we can see, we are now thrown in a more uncertain and complex world. The existing data indicates possibilities of what can be done. These possibilities have to be mapped on the organizational objectives to decide the Digital Transformation plan.
There are more complexities in the Digital paradigm that shown in the diagram. But for the purpose of this article I would like to present a simple view, leaving the complexities for a future and longer article.
This immediately leads us to the four most important points that can help us to get started with AI. This framework does not consider AI in isolation, but the whole Digital Transformation.
The four hints are:
1.    The data that you have: Identify all the data that your company owns. The data can be put into three major buckets:
  • Structured Data: This is the easiest data to identify. It will be found in all the IT systems that you have implemented so far. Also look for the countless excel sheets that your employees have created.
  • Unstructured data: This will be typically text that has no fixed format. Emails, proposals, invoices, challans, vouchers and so on. Hint- even if you might have some of this data in your IT system, there may be ‘left-over’ data. For example, while some part of an invoice is entered in your AP system, there may be some that is not entered. This left-over might contain interesting possibilities.
  • Dark data: This is that part of your data that you never really thought about. In fact, it may not be being captured today. Photos that can take, vibrations that you can record, video that you can capture – these possibilities are endless. No wonder that IBM put the volume of dark data at 80% of all data.
2.    Objectives of your company: There is a whole load of literature on how to identify objectives of your organization and I am definitely not qualified to comment on it, but broadly they will fall in two buckets:
  • Aspirations: what you would like to happen – improvements, new initiatives, lead over competitors and so on.
  • Pain points: what you would like to remove – delays, leakages, inefficiencies.
There will be various areas of your business that will have their own objectives – customer experience, process efficiency, employee satisfaction, innovation, leadership and so on. I will write no more – you are the best judge.
3.    Applications (or Possibilities): The applications of data are of course endless, but since we are developing a framework, let’s again put them in the biggest buckets:
  • Automation: Essentially replacing human efforts by machine. This not only saves cost, but improves accuracy and speed. In most cases, human effort can be diverted to higher cognitive tasks, giving further advantage to the organization.
  • Analytics: Again lots of material is available on this topic, generally categorized in four types:
                    i.    Descriptive
                    ii.    Prescriptive
                    iii.    Diagnostic
                   iv.    Predictive
The most valuable use of analytics is in decision support, for which it has to be combined with Knowledge (see below).
  • Knowledge: The third and often ignored use of data, is also the most difficult to achieve. Combined with automation and analytics this can give rise to spectacular applications. The currently popular use case of chatbots is an example of the Knowledge possibility.
4.    Existing systems: The fourth hint for getting started with AI is studying your existing IT systems. Most of the above possibilities give best results when interfaced with one of the existing system.
I will now try and put these four things in the framework together with help of a simple example.
Let’s say you have an invoice management module in your existing Accounts Payable (AP) system. The scanned copies of incoming invoices are entered in the system by your employees. These scanned documents are the unstructured data.
The possibility this data presents is that of process automation, along with many others. Now, your company has a defined objective of improving process efficiency, and automation of invoice management fits well with that. Since you already have a AP system in place, the forth criterion is also met.
It seems that automation of invoice management is the right problem statement for your company to get started with AI. There will be a few startups and experienced companies who will be able to help you to get started.
The article was first published on the author’s linkedin pulse page, its been re-published here with the author’s permission. 
Author
Devesh Rajadhyax

Founder and CEO, Cere Labs, AI, Machine Learning, Deep Learning

Artificial Intelligence #AI – the new hope for Pharma R&D – By Manishree Bhattacharya @ManishreeBhatt1


Pretty much every article starts with the challenges that pharmaceutical industry across the globe is facing. It is a difficult industry and everybody acknowledges that, considering the time to develop an original drug (10-15 years), the costs involved (last time I checked it was USD 2-3 billion), the high attrition rates of drug candidates (1 out of 5,000 or 10,000 leads make way for FDA approval), the tough regulatory environment which is varied across countries and geographies, and the rising pressures on pricing (pricing advantage for truly outcome-driven therapeutics). All of these, with the looming patent expiry, the imminent entry of generics, and the tantalizing RoIs, make it even more difficult.





Pic Credit: CreativeCommons.org

Well, technology is here to rescue. It will be a grave injustice to talk about technology implementation in pharma R&D in just one article, and because AI and ML are the current buzzwords, I thought, maybe, we could specifically discuss the role Artificial Intelligence plays in streamlining and improving success rates of pharma R&D. Before we go any further, let us have a quick look at the drug discovery process and the typical timelines associated.

More often than not, it takes more than a decade (sometimes > 15 years) for a new drug to enter the market. One should not forget if technologies and in-silico modeling are not effectively used in early stages of drug discovery, drug failure at a later stage is a significant waste of time and money. Now, pharma companies have already been using computational tools to conduct ADMET predictions and in-silico modeling, so what new has Artificial Intelligence to offer?
To answer this question, important will be to look at some of the AI initiatives and activities of key pharmaceutical companies. You may find initiatives where pharma companies are scouting for AI innovation via their open innovation program, so if you are a tech start-up, think about it.

The list is not exhaustive and is only for the purpose of illustration, but one can clearly see that leading pharma companies have already dipped their toes in Artificial Intelligence-mediated drug discovery and development.
Going back to the drug discovery process diagram, let us superimpose the AI-applications across the process.


Artificial Intelligence and Machine Learning algorithms, not only simplify the existing tasks/processes, it saves time, while adding significant value. Let us understand this better.
BioXcel Corporation, a biopharmaceutical company is working on integrating big data and AI into drug discovery process. One such product is EvolverAI which uses AI algorithms for drug discovery to find the best therapy, thus reducing drug failure. Evolver AI uses big data to screen through huge volumes of structured and unstructured data related to genes, proteins, disease pathways, targets, symptoms etc. within the field of Neuroscience. This is followed by creation of meta-data, which contains network-maps, linking pathophysiology of diseases with drugs. These meta-data are then fed into a decision matrix, which compares all the drugs. Using AI algorithms and human intelligence, several hypotheses are built based on the known linkages, of which the best hypothesis gets selected for future experiments and clinical trials, significantly reducing both time and risk of drug failures.

Exscientia, which has partnerships with both GSK and Sanofi, uses AI to learn best practices from drug discovery data, and helps researchers generate drug candidates in much lesser time.
The beauty of such algorithms is in their ability to go though various data-sets – from medical records, publications, clinical trial data, available data on disease pathways and drug-disease correlation to derive meaningful analysis that can help researchers in decision making. Similarly, screening through EHR, current and past clinical trials, and available publications on epidemiology, can help in site selection for clinical trials. Applications are many, and this is still an evolving area, with many developments happening in the stealth mode.
Many diseases such as cancers and neurodegenerative disorders, having high mortality and morbidity, have been debilitating for people across the globe, with pharma companies pouring in significant amount of money, yet with not-so-significant success rates.
Take Alzheimer’s for example, after three large clinical trials for solanezumab, which was called the breakthrough drug candidate, it hasn’t been able to display significant change in patient’s conditions as compared to a placebo. Solanezumab is an anti-Aβ mAbs, that targets Amyloid beta, which is one of the main components of the amyloid plaques found in the brains of Alzheimer’s patients. Does that mean researchers are looking in the wrong place? Is there a need to rethink the disease pathway and establish newer biomarkers to be targeted? THESE are the areas where AI and ML can help.
Similarly, increasingly the drug research community is realizing that not all drugs work on all patients. AI algorithms can help stratify patient population to identify what causes drug efficacy in some patients while nothing really in others – could be due to an aberration, mutation, any specific biomarker – and AI can help recognize the same in the most efficient manner possible.
And that is not all, there is a lot more – we are just scratching the surface, but one cannot deny that these technologies are the next hope for pharmaceutical companies. What do you think?
Source: Nature, Wall Street Journal, MIT Technology Review, Forbes, PR NewsWire, Micar21, SlideGeeks, BenchSci, Xconomy, MedCityNews, Company Websites

The article was first published on Ms. Manishree Bhattacharya’s LinkedIn Pulse Blog, here. Its been republished here with the authors’ permission.

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Manishree Bhattacharya

Independent consulting – strategic research, industry analysis, healthtech evangelist, digital thought leadership (ex-NASSCOM, ex-Evalueserve)
Has over 8 years of experience in strategic research across healthcare, life sciences, software products, and start-ups, and has extensively worked with Indian and Global clients, helping in market analysis, digital evangelizing, start-up collaboration, competitive intelligence, and decision making

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Artificial Intelligence #AI Could Add $957 Billion to Indian Economy, According to New Research by @AccentureIndia


In a recently published report by Accenture, they have highlighted the need for india to invest in AI, we bring you the excerpts of the report. (The following content is sourced from the Accenture report).

Artificial intelligence (AI) has reached a tipping point. The combination of the technology, data and talent that make intelligent systems possible has reached critical mass, driving extraordinary growth in AI investment. Across the world, G20 countries have been building up their AI capabilities. The power of AI starts with people and intelligent technologies working together within and across company boundaries to create better outcomes for customers and society. But India is not fully prepared to seize the enormous opportunities that AI presents. Even with a tech-savvy talent pool, renowned universities, healthy levels of entrepreneurship and strong corporations, the country lags on key indicators of AI development. Much work remains. 


The report, ‘Rewire for Growth,’ estimates that AI has the potential to increase India’s annual growth rate of gross value added (GVA) by 1.3 percentage points, lifting the country’s income by 15 percent in 2035. To avoid missing out on this opportunity, policy makers and business leaders must prepare for, and work toward, the AI revolution. 


The era of AI has arrived. Established companies are moving far beyond experimentation. Money is flowing into AI technologies and applications at large companies. The number of patents filed on AI technologies in G20 countries has increased at a more than 26 percent compound annual growth rate since 2010. Funding for AI startups has been growing at a compound annual growth rate of almost 60 percent.

AI is a new factor of production that can augment labor productivity and innovation while driving growth in at least three important ways:

Mobilize Intelligent Automation
Automate complex, physicalworld tasks that require adaptability and agility.

Empower Existing Workforces
Complement and enhance the skills and abilities of workforces.

Drive Innovations
Let AI be a catalyst for broad structural transformation of the economy. Do things differently, do different things.


The report points out AI is expected to raise India’s annual growth rate by 1.3 percentage points—in a scenario of intelligent machines and humans working together to solve the country’s most difficult problems in 2035

AI TENDING TO INDIA’S HEALTH
India’s healthcare providers have embraced artificial intelligence, recognizing its significant value in better diagnostics with data intelligence and in improving patient experience with AI-powered solutions.

Take Manipal Hospitals, headquartered in Bengaluru, which is using IBM Watson for Oncology, a cognitive-computing platform, to help physicians identify personalized cancer care options across the country.

In cardiac care, Columbia Asia Hospitals in Bengaluru is using startup Cardiotrack’s AI algorithms to predict and diagnose cardiac diseases, disorders, and ailments.

And in eye care, Aravind Eye Hospital is working with Google to use AI in ophthalmology for diabetic retinopathy screening. Also, the government of Telangana is planning to use Microsoft Intelligent Network for Eyecare (MINE), an AI platform, to reduce avoidable blindness, which would make it the first state in India to deploy AI for eye care screening as part of the Rashtriya Bal Swasthya Karyakram program under the National Health Mission.

Accenture, for its part, has developed an AI-powered smartphone solution to help the visually impaired improve the way they experience the world around them and enhance their productivity in the workplace. The solution, called
Drishti, was initially developed and tested through a collaboration with the National Association for the Blind in India.


AI has the potential to have a broad-based disruptive impact on society, creating a variety of economic benefits. While some of these benefits can be measured, others, such as consumer convenience and time savings, are far more intangible in nature. Our analysis focuses on measuring the GVA impact of AI.

Read the press release here >> 
https://newsroom.accenture.com/news/artificial-intelligence-could-add-957-billion-to-indian-economy-according-to-new-research-by-accenture.htm

Read the complete report here >> 
https://www.accenture.com/in-en/insight-ai-economic-growth-india

Author
Team HCITExperts

Your partner in Digital Health Transformation using innovative and insightful ideas

#AI in Healthcare by @deveshrajadhyax


There are some subjects that invoke sharp and contrasting emotions in the society. In present day India, the GST tops the list of such things that are considered boon by some and curse by others. New technology usually does this to people. The steam engine, the telephone and the computer all have been greeted both as the savior and the nemesis of the mankind. 


Using Artificial Intelligence in Healthcare is one such subject. If you have to believe what the media says, AI is going to transform healthcare in the near future. In fact, the services of the doctor may not be needed very often, if at all. AI will do everything in medicine including diagnosis, treatment and even finding out new drugs.

But not everyone is so optimistic or even welcoming. The idea of machines taking care of our health is creepy to some. Others claim that AI can never replace a doctor, at least not in a foreseeable future. Medicine is too complex for machines to figure out.

(You must have noted that I am using Healthcare and Medicine interchangeably. The reason being that this is what most people do. Healthcare is the practice of medicine and as such is wider than it. I refer to healthcare as including medicine.)

The reality, like in the case of GST and most other things, will be somewhere in the middle. The purpose of this article is to find that balanced view. In effect, what I will be saying is:

“while the replacement of the doctor is a faraway dream, there are a number of things that AI can do in medicine even today. This can turn out to be valuable help for doctors, patients and other stakeholders”.


Let me first present a short introduction to AI.

AI, like Philosophy, is a very hard term to define. AI is not really one technology. It is a collection of techniques. Strictly speaking, AI is actually an ambition. The ambition of machines to imitate human capabilities.  
   
But this definition does not take us very far. Since human capabilities are many, ranging from walking to writing poems, imitating any of these capabilities can be called AI. So for our purposes, we will define AI as the pursuit of those capabilities that are strong points of human beings.

As an example, consider language. Reading an article and understanding its gist is a simple matter for us humans. For machines to achieve this capability will be quite something. If that happens, machines can go through a number of articles for us and feed us with the just the little bits that we need.

A whole lot of mathematical and computational techniques have been developed by researchers in the last sixty years to achieve this goal. Deep Learning, Machine Learning and NLP are some names given to a bunch of such techniques. In the last few years, AI has risen to prominence mainly due to three reasons – availability of data, increase in computing power and discovery of new methods. 

Armed with that introduction, let’s try and put down the areas where AI can make a difference in healthcare. While we do that, we can also try to answer the ‘replace the doctor’ question.

Diagnosis: Diagnosis is the hardest part of medicine. There is no definite pathway to diagnosing a patient. A lot depends on the experience and intuition of the doctor, in that way, it is more of an art than science. As of now, it is difficult to see AI taking over this role. However, there are many areas where AI is already making a difference:

  • Conditions in which diagnosis is dependent on analysis of a signal over time, such as an ecg or an eeg. Machine Learning combined with signal processing can achieve good results here. Arrhythmia or irregular heartbeat is an example of such a condition that AI can detect well.
  • Diagnosing some disorders involves referring to a lot of data such as past and present reports, images and history. Gatro-intestinal disorders are notoriously difficult to diagnose and require a lot of information to refer. AI can make a big difference here by sifting through the pile of data and presenting important facts to the doctor.
  • In radiology, the volume of cases is huge and the radiologist needs to look at every image to come to a conclusion. Some investigations like MRI produce a large number of images for each patient. This makes the doctor’s time a bottleneck in handling the ever growing number of patients. Deep Learning has shown great promise in being able to classify medical images. For example, it can separate images that indicate normal functioning from those that have some abnormality. This will enable the radiologist to focus on the abnormal cases first. This method will also be a boon for the remote places where a radiologist is not available.
  • AI has provided a new method for laboratory investigations. This may mean that in the future most lab tests including pathology will be done with basic instruments at a very low cost. In a disease like HIV/AIDS, being able to determine the viral load in a quick and inexpensive way can be a very big benefit to the patients. 


Treatment: The biggest contribution AI can make to treatment of patient is in the area of drug discovery. Currently, discovering a new drug costs more than 2.5 billion dollars and takes more than a decade. The pharmaceutical industry is desperately searching for new ways to reduce the cost and time. AI may be one of the solutions to this problem. Machine Learning and Deep Learning are being used in various stages of drug discovery, such as identifying candidate molecules and studying the expected response of the new drug.

In our fight with cancer, AI may be an important weapon. Personalized Oncology is rapidly getting attention from the medical community as the way forward in battling with the cancer scourge. To describe in brief, cancer is not one disease – the cancer of every patient is different. If the individuality of cancer is decoded, a personal treatment path can be planned for every patient. AI will become a key part of this process.

AI is already playing a role in treatment by making robots that perform surgeries. This contribution will grow in the time to come with the robot costs falling and capabilities growing. This will reduce the strain on surgeons and they will be able to perform far more surgeries in the same time. 

Care: Care during the illness and recovery is as important as the right diagnosis and treatment. Along with IoT, AI will transform patient care. Everything from medicine intake to prescribed activity will be monitored by these systems. Monitoring includes two components – sensing and analysis. While the sensing part is done by the IoT devices, analysis is provided by AI. 

Prevention: Prevention is definitely preferred to hospitalization and AI is going to play a major role in this. It will involve both personal and public health. Personal health is monitored by the wearables and other simple devices. The AI systems will process this data to look for possible indications of disorders so that they can be fixed inexpensively. 

Public health will be monitored in the same way but from data that is coming from various healthcare institutes. This enormous data will forewarn us about various health risks such as outbreaks of diseases. It will enable the state to take measures to avoid the calamities.

In short:
To summarize, AI will really be a transformational technology for healthcare. It will make healthcare cheaper and faster and enable it to reach more number of people. AI will reduce the strain on doctors and nurses. However, for the future that we can see, AI will serve more as an assistant to the doctors, rather than being their replacement. 

Author
Devesh Rajadhyax

Founder and CEO, Cere Labs, AI, Machine Learning, Deep Learning

Four ways in which #AI can help humankind @deveshrajadhyax


Artificial Intelligence is receiving more than its fair share of public attention. On one side there are promises of miracles, while on the other side there are warnings of doomsday. What is probably missing is a simple listing of clear benefits. This is article is an attempt to create such a list.


Artificial Intelligence is more of an ambition than a technology. The ambition is to imitate human capabilities. Since human capabilities range from walking to solving mathematical problems, AI also encompasses systems of various types – ranging from the humble calculator to Google’s DeepMind.

In this article, I am majorly referring to the AI systems that try to achieve the cognitive abilities of human beings. Cognitive abilities refer to the processes of our mind such as understanding, reasoning, planning and selecting the right action. Understanding a question and supplying the right answer from our memory is an example of cognitive ability, the one that AI systems called ‘chatbots’ try to imitate.

Cognitive systems are currently in their initial phase of development. Once they come close to human beings in their competence, they can prove useful to humankind in a number of ways. Here are some:

1. Better utilization of resources: In our current world, it requires a human being to use resources. For example, a car needs a driver. Platforms like Uber have made it possible to share your car when you don’t need it, but it still requires a skilled human being. Talk to your Ola or Uber driver and you will realize that they are already working at the limit of their capacity. A cognitive system driving vehicle will use them much more efficiently. You will actually need much fewer vehicles than you need today (and probably a lot lesser parking!). This is true of most other resources.

2. A fairer society: Human beings have many faults in their thinking. In another article I have highlighted this faults, called biases. These biases have their roots in the evolution, so the AI systems will (hopefully) not have them. ( Pl see http://blog.cerelabs.com/2017/06/will-ai-evolve-to-be-as-bad-as-humans.html). This will make decision making at all level fairer for the people. To take an example, typecasting is a very strong bias that we suffer from. This affects decisions taken by, say an interviewer. We can hope to see much fairer selection processes in the future. For an interesting example of how statistics can help to break biased notions, see the movie or read the book ‘Moneyball’.

3. Repositioning of human efforts: World over, a large number of people are engaged in time consuming tasks that require moderate cognitive ability. Take for instance cooking. A big part of a woman’s day in India is spent in preparing food. Cognitive systems such as robot chefs can easily take over these jobs, freeing up a lot of time that can be invested in more valuable responsibilities like education of the children. In the industries, as the simpler tasks are done by cognitive systems, humans can move up the value chain, pushing the efficiency of the enterprise higher.

4. Improved care: Currently, care of patients, elderly and disabled is primarily a human responsibility. Many times, this compromises the quality of care as people cannot take out so much time from their daily activities. Cognitive systems can make the life of those in need of care much better. They can talk to the elderly and carry the disabled to places otherwise difficult to reach. The systems can keep an eye on chronic patients, not just reminding but making sure their regimen is adhered to.

This is of course just a small contribution to an ever growing list of benefits. While we keep our eyes open to the warnings given by the likes of Stephen Hawking and Elon Musk, we should continue in our efforts to harness the power of AI for these benefits.

Author
Devesh Rajadhyax

Founder and CEO, Cere Labs, AI, Machine Learning, Deep Learning

Understanding the Medical Diagnosis processes, to build an AI based solution by @msharmas


Human Intelligence

is The ability to adapt one’s behavior to fit new circumstances.

In Psychology, human intelligence is not regarded as a single ability or cognitive process but rather as an “array” of separate components. Research in building AI systems has focused on the following components of intelligence: [1]

  • learning,
  • reasoning,
  • problem-solving,
  • perception, and
  • Language-understanding

These components of human intelligence are also utilized during diagnosing a patient and defining the treatment plan and protocol for the patient.


The process of Medical Diagnosis


The process of how a Doctor goes about her diagnoses of a patient, is the ability of a Doctor to adapt to varying presenting illnesses of her patients.

  • Identify the Chief complaint of a patient
  • Gather information about the history of present illness
  • List the possible diagnosis & record the differential diagnosis for a patient
  • And then perform relevant diagnostic tests to determine the most likely causes for the presenting complaints

The Doctor initiates the process of identifying the most likely cause of the patient’s presenting illness and then based on the results of the diagnostic tests, proceeds to confirm a diagnosis and then proceed towards defining a treatment plan for enabling the patient to recover from the disease.
 

In the above simple process defined for a medical diagnosis, the Doctor (based on her training) makes use of all the “components of intelligence” to arrive at the most likely treatment plan for a patient. The process obviously gets more involved and complex depending on the type and nature of diagnosis.

Medical Diagnosis or Medical Algorithms?


From the above “very simple example” it’s clear that the doctor uses her learning and reasoning to proceed towards the best possible treatment pathway for the patient. And this can be treated as a series of Questions that help the doctor arrive at the “confirmed diagnosis” for the patient.
 

The process of Medical Diagnosis can then be treated as an Algorithm that helps the doctor arrive at a conclusion based on the presented facts.
 

Dictionary defines an “Algorithm” as, a process or set of rules to be followed in calculations or other problem-solving operations
 

The doctor in the above scenario has being processing via a set of rules and calculations and problem-solving operations to arrive at the confirmed diagnosis.
 

The doctor goes through a perception analysis to determine what specifically is presented based on the patient’s illness and then determines based on, not only the diagnostic test results, but also based on other parameters of a patient’s active and confirmed diagnosis.
 

Medical Diagnosis work in clinical practice generally has four models: [4]

  • Pattern Recognition, wherein the doctor recognizes the current patient’s problem based on her past experiences with other patients, e.g., Down’s syndrome.
  • Hypothetico-deductive, wherein the doctor performs a certain battery of tests to test a hypothesis, a tentative diagnosis
  • the Algorithm Strategy: the algorithm strategy has been used in Healthcare and has been represented using Medical Logic Modules [5], Arden Syntax for Medical Logic Systems [6] and Clinical Pathways [7] and finally the
  • Complete History Strategy has been defined to be the identification of Diagnosis by possibility. Evidence based medicine is then used to come to a conclusion of the final diagnosis. [8]

The training process to arrive at a Medical Diagnosis has been used in the past to the development of expert systems or Clinical Decision Support Systems (CDSS). Early medical AI systems have tried to replicate the clinical training of a doctor into meaningful implementations of AI in healthcare.

Usecase for Artificial Intelligence in Healthcare


Understanding the process and workflow in healthcare is going to be important in implementing solutions that are “aware” and intelligent. And the systems that need to be developed for Healthcare need to be able to assist the clinicians with systems that are more close to the clinicians natural daily workflow.


Consider the current scenario of a physician meeting with a patient in a clinic setting, with the current systems in place the “Patient Visit” workflow generally involves the doctor having to divide her time between talking to the patient, examining the patient and recording the findings on an EHR (electronic health records) system. Most such visits can last from 5 minutes to an hour depending on the specialty (for instance, general medicine to mental health). Additional complexity is added to the workflow based on the patient diagnosis.
 

There have been many studies that have recorded the doctor’s reasons for resistance to enter the visit data into a system [9]. A time and motion study of a patient – doctor interaction can be revealing in an EHR vs a non-EHR setting. While EHRs have shown their ability to reduce potential errors (as has been well documented in the report, to err is human) the additional steps of transcribing the visit data into an EHR is generally seen by the doctors as being a disruption in their natural visit or encounter workflow.
 

On the other hand, take into consideration a study of the workflow of a pathology department such as biochemistry or hematology, where the technology implementation is relatively easily accomplished. The pathology departments main “Entity”(from a systems perspective) to be processed is the patient sample and the level of automation required to process the various tests that need to be performed on the sample is quite well defined by its degrees of freedom, the test ordered by the doctor. Similarly the entity in a radiology department is the image that is the outcome of a radiology exam.
 

In radiology department for instance, an AI-based solution can enable operations at scale for enabling reading of radiology images from rural areas, where in the images get uploaded by the medical assistant or radiographer at the remote location. The AI systems now have the ability to read and report the images with increasing accuracy, but we still have some way to go before we achieve a greater deal of accuracy.
 

On the other hand, the “Entity” in a patient doctor interaction in a visit, the patient, has many more touch points within the patient care continuum and the level of complexity of this interaction needs to be dealt with in a completely different approach. While the processing in a pathology or radiology department is based on the sample or an exam, which is a snapshot at particular point in time, the treatment of a patient constantly needs to be monitored and presents more data points on an ongoing basis.
 

An AI-based solution to help a physician therefore needs to be applying for instance, the four models of medical diagnosis to a patient visit before we can call a patient visit as an intelligent or aware encounter.
 

If a doctor divides her time between listening to the patient regarding her present illness, and simultaneously recording the information on a computer system, there has been a disruption in the doctor’s natural workflow of focusing on the patient, of listening to her present illness, asking questions about onset, etc. and reviewing the results of the investigations and radiology reports. The doctor is trained to handle all these data points and process the information from the perspective of the four aspects of the medical diagnosis training of the physicians.




Here is an interesting story you would like to review showcasing a doctors 35-hr shift in Delhi, India. By the way the story lends itself to creating some really interesting “Intelligent Digital Assistants” for the doctors.   It also presents to experts developing AI based solutions for Healthcare, a fantastic time and motion study of a Doctors’ shift and the touch points to where the technology can be integrated into the Doctors “workflow”
  
Current systems do not allow that, they tend to focus on implementing a strategy of recording by exception, by recording only the exceptions and all the other aspects being marked as normal, for instance. While such aspects have been proposed and devised by working with the physicians, still they are workarounds to do what the technology of today allows or allowed in the past. 

These are re-creations of paper based systems that have been translated to an electronic health recording system.
 

The Patient – Physician interaction needs to be revamped, in the current information technology systems by enabling the various components of human intelligence we have highlighted earlier:

  • learning,
  • reasoning,
  • problem-solving,
  • perception, and
  • Language-understanding

Ideal scenario for a Patient – Physician interaction would be the implementation of a solution that “records” all of the conversation during a visit and automatically creates the Visit note, by understanding the Chief complaint, presenting illness, history of the patient, procedures ordered, medications prescribed, follow-ups or referrals ordered, et al. Purely based on the conversations between the doctor and the patient.
 

Such a scenario requires the implementation and collaboration between various components of the Artificial Intelligent ecosystem. And that will be the true and useful implementation of AI for the Patient and Physician interaction, enabled by Artificial General Intelligence capabilities.
 

The change needs to be implemented by not only incorporating the changes to the core algorithms, but it also involves incorporating changes to the UI and UX design changes. AI based solutions will force a change in the way current systems have been designed.

Its important to explain the way the physician thinks while interacting with the patient. 

It’s been of late seen technology solutions to be hindering the doctor patient visit process. And hence it my endeavor to try to present the case that AI while hyped to be replacing doctors, is not yet ready for the prime time. There are areas of immense potential, radiology image processing for instance but then that’s from a process improvement perspective. And not doctor patient interaction perspective. 

For years now, technology in healthcare has been trying to take the paperless approach and has tried to “replace” paper while forgetting that there is a more important component of enabling workflow in the Patient Care Continuum. 

And it’s because of this reason, I argue that whilst it’s great for the technology hype cycle to see AI as the deliverer, we need to remind ourselves once again, that it’s not about going paperless, but ensuring the 15 min that a patient gets of the doctor’s time, are well spent with the conversation being patient focused and the technology receding to the background and generating the relevant care records.

In other areas of healthcare too it is about process improvement.

And add to that the fact that in most implementations in healthcare, clinical documentation is either cumbersome or non existent, the hype cycle of AI needs to consider these issues. From my understanding since the underlying data is fragmented, not standardized and not interoperable in majority of the instances; I took a shorter term view of the AI implementation in the systems in this article.

Current Status of Artificial Intelligence in Healthcare

There has been data explosion in Healthcare not only from the perspective of the patient care continuum, but also from the point of view of the resource management and scheduling, inventory and purchase management, insurance, financial management, etc.
 

While most of the current focus has been on building AI-based solutions that are in the patient care continuum, there are definitely many more areas within a healthcare organization that will benefit from the implementation of intelligent systems.
 

Just the other day, I attended a conference around AI and the panelists were mentioning the following uses of AI

  • ecommerce recommendations
  • learning for students based on concepts in school
  • autonomous cars
  • AI based treatments plans for cancer patients
  • intelligent assistants, chatbots
  • Teaching computers to see; etc.

And while they all highlighted areas of advancement in AI tech, they are yet to reach the ability to currently create a system that converts a doctor patient conversation to actionable events that can spawn workflows that needs to be instantiated based on the ever changing patient condition.

In the near-term, I see there will be specialized implementations of AI that will enable the brute power of technology to present the best case scenario for a particular patient condition, but an AI Physician is still a work in progress. This has been shown to be a success with the advent of cancer care solutions using IBM Watson. 

The AI systems are being implemented in various scenarios in healthcare and you could consider them to being “trained” and being presented with a great amount of data and studies. As more data is presented to these AI systems, their level of accuracy will only improve and provide benefits in-terms of scale and reach thereby reducing the time to diagnosis and time to treatment for patients having affordability and accessibility issues in healthcare.

Artificial Intelligence has already started making its way into healthcare, with 90+ AI startups getting funding to deliver solutions like;
  • helping the oncologist define the best treatment plan specific to each patient
  • a virtual nursing assistants, to follow-up with patients post discharge
  • drug discovery platforms, for new therapies
  • Medical Imaging and diagnostics
  • The use of AI in diagnosing diseases, patient education and reducing hospital costs
  • You can also find a great discussion on machine learning, wherein how machine learning could replace/ augment doctors via the health standards podcast with Fred Trotter.

Some of the other areas where AI is being implemented in Healthcare. Microsoft, Apple, IBM and other major players are all looking to AI help in curing people. And they are forming a group that creates the standard of ethics for the development of AI.

AI in healthcare also has a potential to be leveraged to be implemented in the following aspects of Healthcare Industry: 

  • Billing and Insurance Workflow, Insurance reconciliations and provider workflows can be enhanced by enabling total automation of the processes by enabling handling of the insurance claims by AI based Insurance agents. The exceptions and outliers can be escalated for manual interventions and closures.
  • Improving customer experience in healthcare by providing a 360 degree engagement, the SMAC based solutions will use the power of integrating the data streams from multiple sources to help deliver a better service to the patients.
  • Inventory and Supply chain processes can benefit from AI driven optimization by incorporating e-commerce driven innovations that allow for a democratization of product to vendor mix by searching and delivering the best cost options to the procurement department. Thereby bringing the costs down. Logistics improvements delivered in other industries need to come to healthcare to allow for the reduction in the cost of procurement of drugs, devices and durables. AI will help organizations in identifying variable costs and help them understand how to handle scenarios that will present themselves in an ongoing basis.
  • AI enabled resource management and scheduling will allow for identifying areas that need to be staffed with more resources and when additional resources need to be hired to meet with the increasing demands or provide elastic resource management based on ever changing operational demands. Booking appointments with doctors, will become a job taken up by Bots or AI assistants, enabling the nursing and administrative staff to focus more on delivering care and enhanced service experience for the patients.
  • AI-based people management systems will help hospitals in recruitment, retention and performance management of their employees. By presenting an analytics driven approach to people management, systems will be able to help employees to be trained to take up newer roles and responsibilities.

So by when will AI really take over Doctors?
 
It’s clear from the image above, that estimates of how much processing power is needed to emulate a human brain at various levels (from Ray Kurzweil, and Anders Sandberg and Nick Bostrom), “along with the fastest supercomputer from TOP500 mapped by year. Note the logarithmic scale and exponential trendline, which assumes the computational capacity doubles every 1.1 years” [10]. Kurzweil believes that mind uploading will be possible at neural simulation, while the Sandberg, Bostrom report is less certain about where consciousness arises

Based on the above point of view, an interesting question to ask today:
If a Doctor goes through 7+ years of training to become a specialist, how many days will it take for an AI based Physician?

The answer perhaps lies in the following statements

Chief scientist and AI guru Andrew Ng of Chinese search giant Baidu Inc. once put it, “worrying about takeover by some kind of intelligent, autonomous, evil AI is about as rational as worrying about overpopulation on Mars.” [11], [12].

And,

What is it that makes us human? It’s not something that you can program. You can’t put it into a chip. It’s’ the strength of the human heart. The difference between us and machines.
– Terminator Salvation, 2009

References
[1]: AlanTuring.net What is AI?
[3]: Improving Diagnosis in Health Care | The National Academies Press https://www.nap.edu/catalog/21794/improving-diagnosis-in-health-care
[4]: The diagnostic process in general practice: has it a two-phase structure? http://fampra.oxfordjournals.org/content/18/3/243.full
[5]: Managing Medical Logic Modules.
[6]: HL7 Standards Product Brief – Arden Syntax v2.9 (Health Level Seven Arden Syntax for Medical Logic Systems, Version 2.9) http://www.hl7.org/implement/standards/product_brief.cfm?product_id=290
[7]: Clinical Pathways via Open Clinical, knowledge management for medical care http://www.openclinical.org/clinicalpathways.html
[8]: Sackett DL, Haynes RB, Guyatt GH, Tugwell P. Clinical Epidemiology. Boston: Little, Brown and Co., 1991; 3–18.
[9]: Barriers for Adopting Electronic Health Records (EHRs) by Physicians https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3766548/
[10]: Artificial General Intelligence,
[11]: AI guru Ng: Fearing a rise of killer robots is like worrying about overpopulation on Mars
[12]: The Artificially Intelligent Doctor Will Hear You Now
[13]: Why we are still light years away from full artificial intelligence | https://techcrunch.com/2016/12/14/why-we-are-still-light-years-away-from-full-artificial-intelligence/
[14]: AI In Healthcare Heatmap: From Diagnostics To Drug Discovery Startups, The Category Heats Up

[15]: Doctor’s 35-hr shift on 8 bananas, a toilet in nearby cafe
http://indianexpress.com/article/india/india-others/doctors-35-hr-shift-on-8-bananas-a-toilet-in-nearby-cafe/ 
[16]: Gigerenzer’s simple rules by NS Ramnath on Founding Fuel 
http://www.foundingfuel.com/article/gigerenzers-simple-rules/
[17]: A.I. VERSUS M.D: What happens when diagnosis is automated? By Siddhartha Mukherjee
http://www.newyorker.com/magazine/2017/04/03/ai-versus-md

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Is superintelligence the inevitable next step in evolution? by Dr. Roshini Beenukumar, @roshiniBR

https://www.cryptocoinsnews.com/wp-content/uploads/2014/10/ai-handshake-wide.jpg

Experts predict that by 2050, there is a 50% probability that AIs which will match the intelligence of an average adult human. It is not a long wait, isn’t it?


Last week, my husband and I finally sat down to plan our summer vacation. After spending two full hours on 10 different tour websites, we ended up being completely overwhelmed by the options; all-inclusive vs half-board, city vs seaside or sea vs land. 


Decision making is getting more challenging and time-consuming day-by-day. With growing amounts of data and information, there is an increasing need for intelligent systems that could help us make our daily decisions. Several applications use AI to help customers with their decision making, for example, Amazon recommending you the smoothie maker that you always wanted but never bought or Netflix recommending you a new crime series to watch because you are a crime series fan. 

The rise of the “bots”

The AI trend is here and it is going to grow in the coming years. Investors are backing more AI startups than ever. In the first quarter of 2016, there were over 140 equity deals to startups focused on AI, according to CB insights. The spotlight is currently on chatbots and voice assistants. Bot startups like Growbot, Angel.ai, X.ai, Digit, Meekan, Viv and others help their customers with scheduling meetings, tracking employee accomplishments, tracking spending as well as finding the right products and services. This means that, the next time I plan a vacation I could just go to Angel.ai and ask the bot to find me the right vacation package!


As an AI enthusiast, there isn’t a shred of doubt in my mind that AI systems are here to make our lives easier. However, it is hard to ignore issues raised by prominent thought leaders in the field like Stephen Hawking and Elon Musk on the future of AI. Once machine intelligence surpasses human intelligence, will it become an existential threat to the human civilization? 

What happens when AIs start doing AI research?

“Machine intelligence is the last invention that humanity will ever need to make.” Oxford philosopher and leading AI thinker Nick Bostrom, TED, March 2015. 

The answer lies in the concept of intelligence explosion, says Daniel Dewey, an AI researcher from the University of Oxford. The thought experiment goes like this. Imagine that we have created a machine that is more capable than today’s computers. This machine is given the task to improve its current capacity. This leads to a very large and rapid increase in the abilities of these machines. This is called intelligence explosion. To prevent unwanted consequences resulting from such superintelligent AIs, it is absolutely essential that the AI research community collaborate to carefully steer the evolution of artificial intelligence.

Evolution of intelligence on an exponential scale

“It is hard to think of any problem that a superintelligence could not either solve or at least help us solve. Disease, poverty, environmental destruction, unnecessary suffering of all kinds: these are things that a superintelligence equipped with advanced nanotechnology would be capable of eliminating.” – Ray Kurzweil, The Singularity Is Near 

AI is among the top three technologies that is expected to grow on an exponential scale, says Ray Kurzweil, founder of Singularity University. As of now, humans have conquered the lowest calibre of AI called the “weak AI” that specializes in one area, like Google’s AlphaGo beating the world champion in the game GO. The next step in the AI Revolution is a “human-level AI” or “strong AI” with a general mental capacity matching that of a human being. The final step in the evolutionary ladder is postulated to be an ASI or “artificial superintelligence” which is not only smarter than humans but also self-improving. 

A group of AI researchers at companies and leading research institutions around the world are making significant strides in the field of AI. The fruits of their work can significantly change the way we live on this planet. Experts predict that by 2050, there is a 50% probability that AIs which will match the intelligence of an average adult human. It is not a long wait, isn’t it?  

Author
Roshini Beenukumar

Dr. Beenukumar is a molecular biologist turned science writer. During her PhD, she studied how cancer cells behave the way they do by exploring the humble yeast. Currently, she works as a freelance science/technical writer in the Life Sciences industry . She enjoys communicating science to the public and discussing new ideas in the interface of medicine and technology. She spends her spare time getting lost in a book or in nature.

The Current Status of 8 Future Technologies on Healthcare by @msharmas

It’s mid-2016, and here is a look at the current status of 8 Future Technologies that might be having a significant impact on Healthcare


Most if not all these technologies will make an impact on Healthcare, and hence it is important to understand the various scenarios and the stories detailing how the experts from across the world are incorporating these technologies in healthcare


1 Internet of Things

By 2020, there are expected to be 50B IoT devices with a total economic impact of $3.9Trillion – $11.0Trillion across all the industries, out of which $1.6 trillion impact in the “Human” segment.

Experts have identified the various areas in Healthcare, where IoT-based solutions can be implemented in healthcare. 

  • IoT refers to any physical object embedded with technology capable of exchanging data and is pegged to create a more efficient healthcare system in terms of time, energy and cost.
  • Dr. Vikram in his article on how IoT can transform healthcare opined the benefits of remote patient monitoring in emergency cases
  • Dr. Pankaj Gupta, noted in his article for IoT-based solutions to be aggregators of healthcare data from primary, secondary and supporting care market will begin to be aggregated. It will be in the interest of Insurance, Pharma and Govt to support IoT driven Healthcare Market Aggregation
Digital Health startups are working on the following categories as showcased in The Map of Healthcare IoT

  • Clinical efficiency, 
  • clinical grade biometric sensors/ wearables, 
  • consumer home monitoring, 
  • brain sensors/ neurotechnology, 
  • fitness wearables, 
  • sleep monitoring and infant monitoring

IoT platforms need to be created to ensure the utilization of data being generated by the IoT devices deployed in healthcare. Absence of platforms to aggregate IoT device data will result in loss of meaningful and contextual insights being drawn for the patients’ conditions.
 
Here is an Infographic, by Team HCITExperts, IoT in Healthcare, Types of Opportunities

2 Augmented Reality

Pokemon Go happened and augmented reality has triggered the imaginations of the innovators to work on bringing the technology to Healthcare

By 2020, an IDC report states AR – VR revenue will hit $162Billion by offering major applications for healthcare and product design.

In a recently concluded Intel developer conference, Microsoft’s Windows chief Terry Myerson announced a partnership with the chip maker that will make all future Windows 10 PCs able to support mixed reality applications.

For instance, Live 3D imaging is one of the hottest topics in optics today, transforming medical imaging capabilities and delivering the immersive experience behind augmented and virtual reality.  

Tim Cook in a recent interview indicated Augmented Reality to be a bigger market than virtual reality.

3 Virtual Reality

With VR technology projections reaching $3.8Billion by 2020, there will be an increase in the use of VR technologies in Healthcare

Virtual reality has an increasing number of implementation opportunities in Healthcare for education, training and patient treatment.

While the cost of using VR in healthcare is still something that needs to be dealt with, partnerships like the one with Intel and Microsoft only bodes well for bringing the technology mainstream and be cost effective.

VR tech is currently being used to 

  • virtually zoom around the patient’s brain to pinpoint an aneurism before the operation. 
  • 3D virtual renderings of the patient’s anatomy lets physicians get a very real experience before operating on the patient
  • the Virtual Reality is being used to present the patient a virtual human agent that replicates a Doctor & Patient communication, where patients can get their questions answered in an environment free from judgement
  • train surgeons how to use new or unfamiliar devices
  • presenting medical images such CT-Scans and MRIs as 3D renderings for improved accuracy of diagnosis 
  • and as an alternative treatment for seniors

4 Blockchain 
Interoperability in Healthcare is a big topic for debate and a sore unsolved puzzle. With the US HHS and ONC seeking research on Blockchain for Healthcare, there seems to be growing interest in the technology. 

For instance, “By combining the blockchain with the peer-to-peer business model, this creates the potential for a near-autonomous self-regulated insurance business model for managing policy and claims. No single entity would control the network. Policyholders could “equally” control the network on a pro-rata basis” 
– Cyrus Maaghul in Why out of hospital Blockchains matter

Blockchain technology is being researched to be the super secure healthcare data aggregator of EHR data and IoT devices data

Blockchain technology is supposed to benefit healthcare 

  • in population health and clinical studies, 
  • interoperability, 
  • patient centricity, 
  • security,
  • supply chain management 
  • Merck has already announced its exploring the use of Blockchain technology for clinical trials. For instance, if a patient is enrolled for multiple clinical trials, a single blood test common to all the clinical trials needs to be done only once and can be shared across the clinical trial studies the patient has enrolled for.
  • In a recently concluded challenge, ONC in the US announced 15 winners for the use of Blockchain in Healthcare

5 Artificial Intelligence
Artificial Intelligence has been a topic of research all these years, but with the advent of the Data Age, Artificial Intelligence is fast moving mainstream and presents a viable business opportunity. 

“By 2025, AI systems could be involved in everything from population health management, to digital avatars capable of answering specific patient queries.” — Harpreet Singh Buttar, analyst at Frost & Sullivan.

In a recently published report, AI adoption by enterprises is imminent. 38% of respondents are already using AI, another 28% will adopt it by 2018. 

The AI ecosystem is projected to be worth $5.5Billion by 2020

Artificial Intelligence ecosystem consists of:

  • Deep Learning
  • Evidence Based
  • Machine Learning Systems
  • Prescriptive Analytics
  • Natural Language Generation
  • NLP/ Text Mining
  • Predictive Analytics
  • Recommendation Engines

Artificial Intelligence has already started making its way into healthcare, with 90+ AI startups getting funding to deliver solutions like; 

  • helping the oncologist define the best treatment plan specific to each patient
  • a virtual nursing assistants, to follow-up with patients post discharge
  • drug discovery platforms, for new therapies
  • Medical Imaging and diagnostics 
  • The use of AI in diagnosing diseases, patient education and reducing hospital costs
  • You can also find a great discussion on machine learning, wherein how machine learning could replace/ augment doctors via the health standards podcast with Fred Trotter.

Some of the other areas where AI is being implemented in Healthcare. Microsoft, Apple, IBM and other major players are all looking to AI help in curing people. And they are forming a group that creates the standard of ethics for the development of AI.

Finally have a look at the AI in healthcare: Category Heatmap

Source: CBINSIGHTS


6 3D Printing 
3D Printing in Healthcare is making fast inroads in many disruptive ways. The projected market size for 3D Printing in Healthcare as suggested in the IDC report:

“Global revenues for the 3D printing market are expected to reach $US35.4 Billion by 2020, more than double the %US15.9 Billion in revenues forecast for 2016.

This represents a compound annual growth rate (CAGR) of 24.1 percent over the 2015-2020 forecast period, IDC research reports that while 3D printers and materials will represent nearly half the total worldwide revenues throughout the forecast, software and related services will also experience significant growth”

Gartner expanded the number of profiles from 16 in 2014, to 37 technology and service profiles in their latest Hype Cycle for 3D Printing 

3D Printing in Healthcare is being used in the following ways: 

  • 3D Printing and Surgery. All surgical and interventional procedures with complex pathology, extensive resection and/or extensive reconstructions could benefit from this technology: Orthopedics, Cardiovascular, Otorhinolaryngology, Abdominal, Oncology and Neurosurgery.
  • A bespoke 3D Printed model of the patient’s forearm changed the standard course of a 4 hour surgery to a 30 min less evasive soft tissue procedure
  • Affordable prosthetics
  • the FDA has touted the use of 3D Printing in personalised medicine, ans has already cleared 85 medical devices and one prescription drug manufactured by 3D Printing.

Researchers are also exploring the use of 3D Printing which could come mainstream in the future such as Printing prescription drugs at home, Synthetic skin, 3D Printing and replacing body parts.

7 Drones

Last year in a conference a researcher proposed the use of Drones for delivering healthcare in much the same way Katniss receives medicine in the Hunger Games movie or for that matter in the movie Bourne Legacy, UAVs are shown to retrieve the blood samples of Jeremy Renner.

The worldwide market for drones is $6.8 billion anticipated to reach $36.9 billion by 2022

Similarly, there is an active interest in the use of drones to be monitoring traffic, to delivering pizza and products ordered online. 

In context of Healthcare, UAVs are being field tested for transporting samples and blood supplies, medical drone manufacturer Vayu is using UAVs to deliver cutting edge medical technology in Madagascar. In Rwanda, estimated 325 pregnant women per 100,000 die each year, often from postpartum hemorrhage. Many of these deaths are preventable if they receive transfusion via drone delivery in a timely manner. 

In India, Fortis hospital plans on using drones during Heart Transplants, to cut the travel time and save lives. An estimated 500, 000 are in need for organ transplants in a year in India.

Drones & UAVs are also being tested for delivering emergency medical supplies during accidents and natural disasters.

8 Robotics

Robotics in healthcare has been used for sometime now, for instance the Da Vinci surgery system is being used for a myriad of surgeries. 

Just the other day i came across an article on robots being used for some of the tasks at the reception of the hospital.

“Cloud robotics can be viewed as a convergence of information, learned processes, and intelligent motion or activities with the help of the cloud,” the report explains. “It allows to move the locus of ‘intelligence’ from onboard to a remote service.”Frost and Sullivan report on Cloud Robotics.

The overall world market for robotics in healthcare will reach $3,058m in 2015, and expand further to 2025.

The global robotics industry will expand from $34.1 billion in 2016 to $226.2 billion by 2021, representing a compound annual growth rate (CAGR) of 46%.

I was reviewing the articles on Robotics in Healthcare and came across this very comprehensive article Robots/ Robotics in Healthcare by Dr. Bernadette Keefe, MD which provides a comprehensive look at the current and future trends.

Other areas robots are being used in healthcare in addition to the above scenarios are: 

Forrester’s Top Emerging Technologies To Watch: 2017-2021 http://bit.ly/2dmVRkZ  via @GilPress

And there you go, we look forward to you sharing your experiences and thoughts regarding these Future Technologies and share them with our community of readers. 

We appreciate you considering sharing your knowledge via The HCITExpert Blog

Suggested Reading

  1. The Future of Healthcare Is Arriving—8 Exciting Areas to Watch | Daniel Kraft, MD | Pulse | LinkedIn http://ow.ly/KrGS304kGjs
  2. Why the A.I. euphoria is doomed to fail | VentureBeat | Bots | by Evgeny Chereshnev, Kaspersky Lab http://ow.ly/CMKu304kGyU
  3. Looking Back At Today’s Healthcare In 2050The Medical Futurist http://ow.ly/4Dl6304kVZZ
  4. Incisionless robotic surgery offers cancer patients better chances of survival: StudyTech2 http://ow.ly/gpMS304l3wq 
  5. Robots/Robotics in Healthcare | Bernadette Keefe MD http://ow.ly/wRbb304lz44
  6. By 2020, 43% of IT budgets will be spent on #IoT: Jim Morrish, Machina ResearchThe Economic Times http://ow.ly/VKuT304lFi9  
  7. Forrester’s Top Emerging Technologies To Watch: 2017-2021 http://bit.ly/2dmVRkZ  via @GilPress
  8. Are killer bots about to do away with smartphone apps? – http://www.bbc.com/news/technology-37154519 
  9. Where machines could replace humans–and where they can’t (yet) | McKinsey & Company http://ow.ly/v9BY100dNn6 
  10. 2016’s hottest emerging technologies | World Economic Forum http://ow.ly/Jq2R100m4AS 
  11. The Top 10 Emerging Technologies 2016list, compiled by the Forum and published in collaboration with Scientific Americanhttp://www3.weforum.org/docs/GAC16_Top10_Emerging_Technologies_2016_report.pdf 
  12. Rwanda’s hospitals will use drones to deliver medical supplies http://money.cnn.com/2016/10/13/technology/rwanda-drone-hospital/index.html?iid=hp-toplead-intl 
  13. 4 Trends Shaping The Future Of Medical Events https://t.co/rUUUJ7oqkK #digitalhealth #hcsm https://t.co/KuPgGW4k9Z 
  14. Post-PC Tech Rules at Intel Developer Forum 2016 https://lnkd.in/fKux3Ek 
  15. House MD vs Doctor #AI- Who will turn out to be the better by @RoshiniBR http://ow.ly/elXy304mYpv

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    House MD vs Doctor #AI- Who will turn out to be the better by @RoshiniBR


    House MD vs Doctor AI- Who will turn out to be the better diagnostician?


    Do a google search for “all-inclusive beach holiday” and all you see for the next weeks are advertisements for all-inclusive holidays following the virtual you. Google knows you better than your family or friends- creepy but true! Whether or not we realize it, we have made the decision to donate our data to the virtual world.

    Can we put our data to better use – to improve healthcare on a scale unimaginable a decade or two ago?


    Artificially intelligent systems in healthcare

    AI systems feed on big data. Big data is nothing but a massive amount of data, the sheer size of which makes data analysis a challenge. AI systems are being developed to analyze and recognize meaningful patterns out of this complex data. Of particular significance is deep learning, a branch of AI which attempts to mimic the thinking part of the human brain. Several startups are now attempting to put AI, particularly deep learning to meaningful use in healthcare.

    Diagnosing diseases: Clinical diagnosis is essentially a data problem, says founder of Enlitic, a machine learning startup focusing on data-driven medicine. They aim to revolutionize clinical diagnosis by helping physicians to automatically screen for specific diseases using their proprietary technology. Another startup, Deep Genomics, is approaching diagnosis from a genomic perspective. They capitalize on machine learning technology to link genetic variations to diseases. As a cherry on top, a study published as recent as last week reported that AI-enabled automated cancer detection perform as well as approaches that require costly clinician input.

    “We think that its no longer necessary for humans to spend time reviewing text reports to determine if cancer is present or not”- author of the study, Shaun Grannis M.D., M.S.


    Educating Patients: IBM is teaming up with the American Cancer Society to create an adviser for cancer patients, powered by IBM’s Watson health – health division of its AI brain. It would be designed to provide cancer patients or their caregivers with personalized guidance based on the patient’s particular disease stage and treatment. Watson would sift through countless digital sources like health websites and draw relevant and trustworthy information catering to individual needs. 

    Improving clinical trials: Many clinical trials fail because patients fail to take their medications. AiCure is addressing this issue using AI to monitor medication adherence thereby increasing trial success.  They directly monitor patients using artificial intelligence on mobile devices via an app which collects real-time dosing data on a centralized and cloud-based platform. IBM Watson is trying to solve another issue facing clinical trials – enrolling patients. Watson would sift through clinical trial data at Mayo Clinic and in public databases, such as ClinicalTrials.gov. and match patients more accurately and consistently to clinical trial options.

    Using Watson’s cognitive computing capabilities, Mayo Clinic can consistently offer more cutting-edge medical options to patients and conclude trials faster”  – Mike Rhodin, senior vice president, IBM Watson Group.


    Accelerating drug discovery: Drug discovery is a lengthy, complex, and costly process, rooted with a high degree of uncertainty that a drug will actually succeed. Several machine learning startups are trying to solve this long-standing predicament in pharmaceutical drug development. Berg Health combines systems biology with its proprietary artificial intelligence machine learning analytics program to bring down the time for cancer drug development to almost half. Atomwise’s drug discovery AI platform “learns like a human chemist” using deep learning algorithms and supercomputers.

    “Simulating billions of virtual medicines to find potential commercial candidates in weeks? That’s what we call truly transformative.”- Matt Ocko, Managing Partner of Data Collective, a venture firm backing Atomwise


    Reducing hospital costs: Readmissions are costly for hospitals as payers are reluctant to reimburse preventable readmissions. Hindsait, an artificial intelligence technology provider, helps identify patients who are nearing the end of their stay and suggests whether keeping them might in fact be more cost effective than risking readmission. It does so by applying AI based data analysis to large health datasets.

    These and many more upcoming developments in cognitive computing in healthcare leaves us with the thought – will AI systems perform better diagnosis than out best diagnosticians. Google’s AI program, AlphaGo beating Go world champion and IBM’s Watson beating humans in Jeopardy seems to suggest so.

    Note: Interestingly, there were many recent publications that influenced the direction of this article. Application of AI technologies in healthcare is growing with new collaborations being made at this very moment. Interesting times indeed for all those who influence and follow these developments. 

    Author
    Roshini Beenukumar

    Dr. Beenukumar is a molecular biologist turned science writer. During her PhD, she studied how cancer cells behave the way they do by exploring the humble yeast. Currently, she works as a freelance science/technical writer in the Life Sciences industry . She enjoys communicating science to the public and discussing new ideas in the interface of medicine and technology. She spends her spare time getting lost in a book or in nature.

    Benefits of an AI-Based Patient Appointments service for Hospitals by @msharmas


    One of the areas where AI can be implemented in the Hospital with high volume of transactions, is the Appointments Scheduling of Patients. On any given day, there are a finite number of slots available for a doctor, e.g. 10 min or 30 min slots, depending on whether its a first visit or a follow up visit. In most hospitals, Routine patients are scheduled in advance and some patients are scheduled based on an urgency, to the physician schedule.  [Denton et al – 8]
    A typical workflow for booking an appointment can go like this:

    1. Patient calls (or visits) the hospital, and speaks to the person at the reception, at a specific department
    2. The person looks up the available time slots, that a doctor is free and available in the clinic
    3. Consults with the Patient on the best time possible for her appointment and then schedules the appointment



    Now this three step process can either happen on a call, at the hospital reception or via a website provided by the hospital. But in real life, the appointment booking process for a patient might not be so straight forward. Here are some of the different scenarios that might occur:

    1. Doctor is not available, asks her medical assistant to cancel all her appointments. Existing appointments need to be shifted to other doctors or rescheduled based on patient priority.
    2. Patient calls at the last moment and asks for her appointments to be re-scheduled or cancelled
    3. Patient does not show up for the appointment, and asks for a new appointment
    4. During a clinic day, multiple new and urgent cases need to be seen by the physician, which delay the subsequent appointments
    5. Scheduling of renal therapy patients or cancer therapy patients also needs supervised scheduling that is closely related to the patients’ care protocols and care plans
    6. Scheduling based on urgency and emergency situations also changes the “scheduled” visits of a doctor 
     
    Considering these challenges in the daily working environment of a hospital, an AI-based scheduling solution can help the hospitals in providing an optimal use of resources. For instance a research from Indiana University [4] found using Artificial Intelligence in patient care can be cost effective and improve patient outcomes.

    Consider for instance the following statistics of a Government Hospital in Rajasthan, India [6]:

    • Nearly 1.27 crore patients were registered at OPD in medical centres affiliated to medical colleges and
    • 9.27 crore in state medical institutions in the year 2014-2015
    • in the year 2015 around 35,000 patients per day were registered at the OPD at medical college-affiliated centres

    The High number of patients (the 35,000 per day patients registered at the OPD at medical college-affiliated centres) and the resource scheduling scenarios, presents an apt usecase to implement an AI based Appointment Scheduling system.

    While it not only present a challenge to manage the care of all the visiting patients, it also allows for the administration to ask; How many doctors, nurses and medical assistants should be scheduled to manage the care planning & scheduling requirements of each of these patients, visiting one or many departments of the hospital.

    In addition to Patient Scheduling, AI based algorithms can be deployed in such settings [2] to help the hospital administration in optimising the time of their most important resources: Physicians, nurses and medical assistants.

    Handbook of Healthcare System Scheduling – Reference [7]

    Additional Scenarios where the AI based resource scheduling systems in Healthcare [7] can be deployed are:

    • Operating Theatre + Operating Team Scheduling
    • Renal Dialysis Centers
    • Radiology Diagnostic Facilities
    • Medication Reminders Apps
    • Acuity-based nurse assignment and patient scheduling in oncology clinics
    • Care Plans based activity & event scheduling
    • Procedure Scheduling
    • Health Checkups Packages

    Once an AI based solution has been implemented, the scheduling, rescheduling, planning, allocating and many other scenarios are handled by an AI based Scheduling Agent allowing for hospital administrators and physician scheduling managers to focus on treating the patients. 

    And Scheduling a patient appointment becomes an autonomous process:

    A. Jane emails Dr. John to schedule an appointment for a followup visit. Jane receives a confirmation email regarding the appointment with Dr. John from his assistant Amy. A reminder is set in her calendar.



    B. Jane, on the day of the appointment is unable to make it to the hospital and sends an email requesting for rescheduling her appointment to the next wednesday. Amy reviews, Dr. Johns schedule and responds to Jane with a confirmation of her re-scheduled appointment.

    In the above example Amy is an AI assistant to the Physician, nurse or medical health professional. Or in fact it could be an assistant (Siri, cortana, or amy from x.ai etc) to the Patient.

    What do you think, do share your thoughts?


    Sundar Pichai, CEO, Google says we are moving from a mobile first world to an AI first world, quite fast.

    References

    1. How to use AI to automatically schedule your appointments with x.ai – TechRepublic http://ow.ly/lSdJ300yH9w 
    2. [1206.1678] A Distributed Optimized Patient Scheduling using Partial Information http://ow.ly/u3A0300yHu3 
    3. Artificial Intelligence in Healthcare: A Smart Decision? | Health Standards http://ow.ly/IR3N300yIep 
    4. Can computers save health care? IU research shows lower costs, better outcomes: IU News Room: Indiana University http://ow.ly/bPWs300yIs6 
    5. Association for the Advancement of Artificial Intelligence http://ow.ly/4aoc300yIxY 
    6. E-registration Facility Soon At SMS HospitaleHEALTH | EHEALTH http://ow.ly/njMx300yJgz 
    7. Handbook of Healthcare System Scheduling – http://ow.ly/cvUn300yLql 
    8. From Scheduling Meetings To Shopping Deals: 14 Early-Stage AI Assistants To Watch http://ow.ly/R9b7301lqjK
    9. Who will turn out to be the better diagnostician? #digitalhealth #ArtificialIntelligence https://t.co/TmzInbDlg5
    10. Robot Takes On Role Of Hospital Scheduling Nurse | Digital Trends http://ow.ly/QTAW100eEgR
    11. This is how the future of hospital operations resembles air traffic control – MedCity NewsMedCity News http://ow.ly/BJh1100eIdv
    12. Can Artificial Intelligence Help The Mentally Ill? https://t.co/e5NEnYOpAL #mentalhealth #AI
    13. On-line Appointment Sequencing and Scheduling – Brian Denton et al, http://ow.ly/RXXm300yLHX
    14. Artificial Intelligence Can Improve Healthcare | EMR and EHR http://ow.ly/MlBy302ur9Q


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    Zen Clinicals: An Activity & Workflow based solution (1 of 4)

    Part 1 of 4:

    Recently, during the Gartner Symposium, it was predicted that cognitve platforms would take over a lot of activities. Keeping this future at the back of our minds, its important for the EHRs of today to metamorphosize to the Cognitive Computing platforms of tomorrow, and fast.

    With social media, predictive and bigdata analytics become more central to the discussions; the current EHR systems are woefully lagging behind in the ability to catchup with the technology of today. Newer EHRs or healthcare based platforms too are lagging behind in the adoption of these technologies. Most often have observed the similar functionality being rolled out on newer technology.

    In most cases I have found the solutions to be non-workflow, i.e., non-BPM, enabled solutions that have the inability to adapt to a new healthcare scenario faced by the solution in the next implementation. With the advent of the Software as a service platforms and the proliferation of the cloud based services in other industries, healthcare solutions have generally gone for the traditional (non-BPM based) approach to delivering a solution. 

    So, One needs to then ask, “What should the future of a patient health record (PHR) or the electronic health record look like?” 

    Will we really see the advent of SMART solutions in healthcare, or are the changes to difficult too incorporate into the solution because that’s how the solutions had been developed?

    In this series I present some thoughts on what should the solutions of today morph into to meet the needs of the users and hopefully make it more user-friendly in the process.

    And to develop the solution, Zen Clinicals (this is a fictitious name of a solution any resemblance is purely a coincidence), we need to answer a few questions upfront:

    • who do you build it for? 
    • Who are the actors? 
    • And what do they need? 
    • What are their activities? 
    • What do they do daily?
    “User Experience strategy lies at the intersection of UX design and business strategy. It is practice that, when done empirically, provides a much better chance of successful digital product than just crossing your fingers, designing some wireframes, and then writing a bunch of code”

    UX Strategy by Jamie Levy (O’Reilly)



    Lets explore the actors in a small clinic. And list out the answers to the questions.

    The Actors: 

    • Physician
    • Nurse
    • Billing Person
    • Customer Service Person
    • Customer (Patient)

    The Activities:

    Its is important to understand the way each of these people work. Its a mix of their training (the way each of these people perform their tasks) and the requirements of their roles. 

    We define an Activity as 

    Lets take them up one by one. 


    The Physician Activities:

    The Physician is the focal point from the operations perspective. What does she need? 

    For this lets consider Dr. Jane’s typical day. She gets ready and heads out for work. Reaches her office, marks her attendance at the biometric scanner. She then heads out to her office and logs into her system.

    On the way to her office, the nurse Jenny informs her of the number of patients that are scheduled for the day and the number of patients that have already arrived.

    Dr. Jane, checks out her emails before starting to see her patients. She then heads over to the system to view the list of patients that are waiting to see her. Before she calls on the first patient, she reviews the patient records from the previous visit to bring herself upto the current status of the patient.

    She calls her first patient.

    The Nurse activities: 

    The nurse is a master tactician who works as the floor manager in the clinic. She handles the multiple schedules, that of the physician and the patient. While the doctor focuses on treating the patient, the nurse handles not only the administrative work related to that patient, but also the clinical preparations that a patient might need pre or post seeing the doctor. 

    Nurse Jenny, started her day by arriving at the clinic about half and hour before the clinic was to open. She checks up on the list of appointments scheduled for the day and orders the relevant medical records as per the way the patients are scheduled to arrive for their appointments.

    Dr. Jane arrives at the clinic and Nurse Jenny informs the doctor about her appointments for the day and any other Administrative aspects that needs the Doctors attention.

    Meantime, the first patient scheduled for the appointment, calls the nurse to inform if the doctor will be able to consult her via video conference. Nurse Jenny schedules the video conference for the patient with Dr. Jane.

    The Billing Person’s Activities: 

    The billing person arrives at the clinic and decides to check on the status of the outstanding dues, the patient ledger of the patients visiting the clinic today to review their insurance details and the plan of processing first time patient insurance details.

    The billing person also completes the coding activity for the bills that are to be processed. The billing person sends the billing statement to the uninsured patients who owe a balance for their visit. The billing person also issues the billing statement to the insured patients once the insurance company processes their claim.

    The Customer Service Person

    Elaine starts from her home to the clinic and is immidiately alerted by the customer relationship management system regarding the various appointments that are scheduled for the day. She selects the option to bulk message reminding each of the customers regarding their appointments and the time the customer should arrive at the clinic to attend to the appointment.  

    Before, she reaches her office, all the reminders messages to the patient have been sent on their way. And of course, yesterday she had already called each of the customers reminding the customers regarding their visit and the documents the customer should bring along with them for the visit.

    The customer service person is responsible for taking care of the patient appointment reminders, customer satisfaction surveys, patient engagement via social media and many other activities as required by the clinic.

    The needs of the clinic will define the various campaigns that the clinic runs for their patients to develop an in-premise and off-premise customer relationship management processes. 

    The customer service person is also responsible for maintaining a constant conversation with the patient to enable a superlative customer experience.
     

    The Patient Activities

    The customer Jennifer logs into her portal and schedules an appointment with Dr. Jane. She uploads her insurance details as part of her appointment details

    Jennifer, attaches the latest reports, relevant for this doctor visit and the medications she is currently taking.

    One day prior to the date of her appointment, jennifer gets a call from the clinic confirming her appointment and updates on any other documents she needs to carry with her)
       
    The patient is the most important aspect of the entire healthcare workflow. Each of the activities and the processes within the hospital are moving towards a patient (customer) facing than being hospital or doctor facing.

    The Patient starts her journey by anyone of these scenarios:

    • booking that first appointment with a doctor in a facility
    • booking for a health-checkup
    • booking for a radiology or pathology service, as referred to by a GP
    • and many others…   

     

    Activity Interactions

    Now that we have identified the various actors in a clinical setting it is important to understand the interactions between these actors. There is a need to consider the interaction between the actors to be connected to other actors and at other times the activities could be limited to a single actor.
     
    However, the interactions between various actors also define another dimension to the entire workflow. For instance, the doctor – nurse interaction could have the nurse as the focal point and at other times the doctor as a focal point of interaction. Hence the activities that will be delivered to the user based on her role will depend on the interaction context.

    Using the activity interaction map, the system will be able to setup the base set of activities and then progress from there to learn about any new interactions and activities to be performed. 

    Newer activities are coded into the system by defining newer objects and the front end definitions will result in the generated page engine to present to the user the screen in which they need to enter the information. This will be achieved by using a combination of an object creator, rules engine and front end designer.

    Additional Considerations

    To develop a solution of this nature we present a list of features that should be included into the framework of the solution
     
    Generated Pages: Using cognitive computing, the system will be able to present the doctor a Patients Health Record as a generated Generated Page which displays the details of the patient record on the basis of the current visit and diagnosis. Included in this generated page are actionable intelligence inputs presented to the doctor by the underlying congnitive computing enabled analytics platform.

    Activities presented to the care provider on the basis of the Appointments, ward rounds, patient interactions via virtual visits – doctor is presented with the patient information when the visit is started.

    The doctor should be able to type in regular statements and the system responds by pulling together relevant information regarding the patient. For instance, the doctor queries, “display the list of active medications the patient is on and display the list of lab tests that have been ordered in the past three months”. 


    Speech Recognition and NLP: The Zen clinicals will heavily employ the speech recognition and NLP capabilities to allow the doctor to perform the following activities. It is important to get the doctor to move away from the system and focus on the patient treatment

    • The doctor will be able to dictate the details of the visit as a recording. While dictating, the system will alert the doctor regarding any mandatory information that is required for the visit, that had been missed out in the dictation. 
    • Secondly, the system will allow the doctor to see the mandatory form that needs to be filled for the patient visit or his ward round and indicate by voice commands the values that need to be selecteddeselected or chosen from a list of values etc  

     
    Digital Assistant: The Zen Clinicals solution will have a digital assistant available for the doctor to help in performing any of the activities. These activities could be a resulting microinteraction or they could be statements submitted by the doctor to the system. Activities can be performed
     
    Data Analytics: At the time of prescribing the medication for the patient, the doctor is presented with the list of medications sorted by their administration to other patients under similar parameters, by cost and availability at the patient location. The Zen Clinicals solution will be developed with a analytical data structure at its core.


    Microinteractions: The system should have the ability to allow the users to define microinteractions as a set of rules and trigger criteria to generate activities or alerts based on the rules and trigger criteria. 

    Alerts Engine: At the core of the Zen Clinicals framework is also an alerts engine that takes keeps track of all the results generated from any microinteraction and has the visibility of delivering these alerts via multiple channels (desktop alerts, mobile notifications or wearable notifications). The alert engine uses the presence definitions from the unified communications framework component to deliver the alerts to the user

    Workflow Engine: The Zen Clinicals framework incorporates a workflow engine at its core to define the various workflow activities, such as authorizations, digital signatures and sign-offs for authorization activities, co-sign actions for activities, peer review and sign-off, order process workflows and many other such definitions.
     
    Unified Communications Platform: The Zen Clinicals solution has a unified communication platform integrated into the core of the framework to enable the presence identification and communications framework from within the application. The users will be able to make use of multiple communication channels using this capability to share information seamlessly with their peers.
     
    Face Recognition: 50 Face Recognition APIs – Data Science Central http://ow.ly/VNvhI
     

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