Sophistication in AI/ML application for Diabetic Retinopathy diagnosis has tremendous research scope by Anusha Ashwin, @ashwin_anusha

October 25, 2020 / AI/ML/DL / 9 minutes of reading

India – better known as the Diabetes Capital of the World – has been battling with this non-communicable lifestyle associated disease for long. According to the International Diabetes Federation, India’s diabetes population totalled 74 million in 2017 and it is estimated to reach 134.3 million people by 2045.

Diabetes in India is an epidemic that presents itself with several other complications when left uncontrolled or untreated. And notably, complications in the eye and vision are the most commonly associated negative impact of diabetes in India. Among the complications in the eye, the most prevalent type of diabetic eye diseases is Diabetic Retinopathy – which on early diagnosis and intervention is completely curable.

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Sophistication in AI/ML application for Diabetic Retinopathy diagnosis has tremendous research scope by Anusha Ashwin, @ashwin_anushaRead More »

Leveraging NLP To Analyse Effects of Covid-19 On Baseline Demographics and Barriers to Compliance By Arpita Dutta – Student Author

September 6, 2020 / AI/ML/DL / 11 minutes of reading

It has been more than 8 months now since the COVID-19 pandemic. With cases skyrocketing, everything remains very uncertain till now. What we do have now is information, data and huge amounts of research going on globally (Kaggle currently has 220K scholarly articles which is approximately 17GB of data) Healthcare is a very dynamic field as many parameters keeps changing. It becomes difficult to keep a track of this, because there is a huge amount of data online. But how is one supposed to analyse such huge amounts of data with a plethora of information out there? Being a healthcare student, I realised that this is not just COVID-oriented problem, wherever there is data there will always be the goal to analyse it. But how easy would it be to analyse this data and provide a conclusion?

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Leveraging NLP To Analyse Effects of Covid-19 On Baseline Demographics and Barriers to Compliance By Arpita Dutta – Student AuthorRead More »

Leveraging Machine Learning Applications for reducing the drug discovery time by Pragya Sharma – Student Author

August 17, 2020 / AI/ML/DL / 9 minutes of reading

The entire world has come to a standstill in 2020 due to the spread of COVID-19 pandemic. According to an article published in march 2020 the major cause of this panic is that COVID-19, the illness caused by a severe acute respiratory syndrome known as SARS-coronavirus 2, is much more contagious and deadly as compared to other infections, furthermore, there are no approved treatments or vaccines. Currently there are no FDA approved drugs or vaccines for the prevention and/or treatment of COVID-19 (1).

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Leveraging Machine Learning Applications for reducing the drug discovery time by Pragya Sharma – Student AuthorRead More »

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

February 25, 2020 / AI/ML/DL / 3 minutes of reading

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.

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Explainable artificial intelligence in healthcare by Dr. Johnson Thomas, MD, FACE, @JohnsonThomasMDRead More »

Use of Artificial Intelligence in Healthcare Delivery by Dr. Sandeep Reddy, @docsunny50

February 3, 2020 / AI/ML/DL / 32 minutes of reading

Abstract

In recent years, there has been an amplified focus on the use of artificial intelligence (AI) in various domains to resolve complex issues. Likewise, the adoption of artificial intelligence (AI) in healthcare is growing while radically changing the face of health- care delivery.

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Use of Artificial Intelligence in Healthcare Delivery by Dr. Sandeep Reddy, @docsunny50Read More »

The case of #AI medical software regulation in developing countries by Dr. Sandeep Reddy, @docsunny50

May 14, 2019 / AI/ML/DL / 5 minutes of reading

Has the cart been placed in front of the horse? The case of AI medical software regulation in developing countries.
Medical software is defined as the use of software for medical purposes. The uptake of medical software in healthcare has increased in line with increased application computation in healthcare delivery. Examples of medical software include software used in bedside monitors, MRIs, PACs, radiation therapy software, infusion pump rate devices, smartphone-based health applications. Etc.

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The case of #AI medical software regulation in developing countries by Dr. Sandeep Reddy, @docsunny50Read More »

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

December 18, 2018 / AI/ML/DL / 5 minutes of reading

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

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

August 16, 2018 / AI/ML/DL / 7 minutes of reading

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

The young doctor who first advised me maybe had never seen such symptoms earlier and thus was not able to diagnose correctly.
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.
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.
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.

Natural Language Processing #NLP – Giving doctors the freedom to write what they want by Dr. Anuradha Monga

August 16, 2018 / AI/ML/DL / 7 minutes of reading

Healthcare produces the highest quantity of data records as compared to any other industry. There has been a substantive shift in the provider workflows from capturing data in paper based records to electronic modes and storage in the past few decades.

Natural Language Processing: Giving doctors the freedom to write what they want

Electronic health records (EHRs) have clearly emerged as an innovative technology to facilitate the transition. However despite of the advancements, EHRs have not been able to achieve credible benefits in areas of population health management, health information exchange, patient care coordination and clinical analytics.

One of the biggest barriers in achieving success with EHRs has been the disparate forms of data which are difficult to aggregate and analyze. Doctors feel comfortable writing notes along with the flow of their clinical thoughts, however EHRs are not designed to capture medical information in a doctor’s natural language. This inability many a times leads to poor EHR usability. As a result, a lot of valuable information is left out from the ambit of analysis. With the advent of newer technologies, now it may be possible to plug such gaps. NLP (natural language processing) is one such technology which providers are now adopting with an anticipation to improve clinical outcomes and for the simplification of the daunting task of data entry in a computer.

Clinical data is not consistent, making analysis difficult

An EHR captures data in primarily four ways:

Clinical Data is directly entered in pre-structured templates
Scanned documents are uploaded in the system
Text reports are transcribed by speech recognition technology or by dictation and manual data entry.
Data is purged into an EHR by interfacing it with other information systems like laboratory systems, radiology systems, or monitoring devices.

Clinical data is usually presented in a structured or unstructured format. Selective choices for capturing data in the form of templates like physician order sets, drop down menus, check boxes etc constitute structured data. Aggregation, analysis and reporting from structured data is easier but doesn’t provide an individualized, customized identity to an EHR. On the other hand, unstructured data constitutes free text narratives and clinical notes i.e doctor’s notes, patient encounters, patient health records etc and enable the physicians and patients to get their observations, complaints and concepts recorded in their own parlance. The unstructured data is a rich source of information about a patient’s health but it’s a challenge to transform it into structured and analyzable data that can be used for improving care outcomes. This challenge can be overcome with the technology of natural language processing.

Unstructured clinical notes are a mine of golden data; the wait to explore them ends with NLP

NLP is a data science based technology that can extract data from free text. NLP can be used by clinicians to convert medical notes into formats which are structured and standardized. Auto-processing of textual data can help providers in making use of clinical documentation data for a variety of purposes including but not limited to:

Improving communications between healthcare teams and thus help improve outcomes
Reduce overhead costs of clinical documentation
Improve revenues by automation of the coding and documentation

Computers can be given the ability to infer the intended meaning of words, thus enabling them to identify trends and patterns in huge datasets.

NLP can change the course of the way chronic diseases are managed:

One of the most promising area for exploring use cases of NLP in healthcare includes predictive analytics and risk scoring. Carefully deployed AI tools can be used for risk stratification and determination of hotspots in chronic diseases.

NLP can be used to tag socioeconomic terms hidden in free text notes to identify the social determinants of health. This can be augmented with machine learning to develop risk scores by proactive identification of trends from clinical and social data, laboratory reports, diagnoses etc. It is possible to create algorithms and train them on clinical record data to identify disease symptoms accurately.

Clinical records are a rich source of information regarding the symptoms of many diseases. Grouping of such similar symptoms can help in syndrome identification on the basis of disease presentation. As a result, it may be possible to unearth clusters which may otherwise not be suspected. Routinely available information in electronic health records, such as demographic and geographical location data and primary care free-text clinical records should be leveraged while making use of such algorithms.

Why off the shelf NLP engines may not be what the doctors want:

While it sounds easy, healthcare free text data comes with its own challenges. Word sense ambiguity is perhaps one of the most challenging problems in the noise of free text clinical notes. Accurate translation of the structured patient information pertaining to medical procedures, symptoms, tests etc depends on the algorithm’s ability to assign correct interpretations to the relevant medical words. For example, the acronym RA can be used in different contexts with different meaning by doctors. RA can be interpreted as right atrium, right arm or rheumatoid arthritis depending on the case presentation and clinical context.

Disambiguating the senses of acronyms, symbols and words that are used in a doctor’s clinical notes can significantly ease the burden on human effort needed to develop more accurate systems. A data-driven approach which involves development of any algorithm that infers patterns should consist of a supervised and unsupervised learning phase to yield benefits. In supervised learning every data item of the training data is labeled with the correct answer. Unsupervised learning on the other hand is a process where the computer recognizes patterns automatically. The true potential of an NLP and machine learning algorithm can only be harnessed when the data is trained in the provider’s environment.

Word sense disambiguation based NLP pays a significant role in improved analytics and patient outcomes:

Word sense ambiguation based language processing ability of the computer for accurate mining of clinical documents can bridge the gaps in documentation and aid clinical decision support and clinical documentation improvement programs.

More insightful extraction of data is possible with a decreased ambiguity in clinical data. When the computer has the ability to infer the intended meaning of words, it can find useful patterns in heaps of data easily. IBM’s Watson Supercomputer technology is an apt example of how NLP can facilitate meaningful analytics, by identifying such patterns. IBM’s content analytics process is used for collection and analysis of structured and unstructured data, and its similarity analytics makes use of NLP and machine learning technology for analysis of a large number of variables in a patient’s medical history and present condition to identify patterns and draw a comparison with similar conditions and potential outcomes.

There is no doubt that word sense disambiguation enabled NLP technology can have a potentially huge on impact clinical data analytics with its superior ability to infer meanings of extracted data more accurately. Data analytics for improved patient outcomes is not the only benefit of this technology, it can also support accuracy of billing. With its ability to support clinical documentation improvement programs, it can also help in improving clinical workflows.

SymptomAI by “PredictDisease” is a healthcare analytics platform that is driven by artificial intelligence, NLP and machine learning to assist patients and primary care physicians by measuring the potential risk of a chronic disease that starts with minor symptoms. The platform leverages data from lifestyle activities, social media/website forums, scientific research papers, and family history, matching these with known signs/symptoms and other demographic characteristics for the early detection of the chronic disease. It takes into account, social and biologic determinants of health to predict the risk score. Visit us at www.predictdisease.com or write to us at info@predictdisease.com for more info.

References:
[1]. Auto Coding and NLP:
http://www.himss.org/content/files/AutoCodingandNaturalLanguageProcessing(WhitePaper).pdf

[2]: Dooling, Julie A. “Advancing Technology Connects Transcription and Coding: The Developing Role of NLP, NLU, and CAC in HIM.” Journal of AHIMA 83, no.7 (July 2012): 52-53

[3]: Goldberg, Michael. “IBM Makes New Health Care Push with Predictive Analytics, Process Management.” Data Informed. http://data-informed.com/ibm-makes-new-health-care-push-with-predictive-analytics-process-management/

Author

Dr. (Maj) Anuradha Monga

A versatile military veteran with expertise in healthcare management, Anuradha has acquired real world experience in areas of Hospital operations, Health insurance claims management and mass insurance, Healthcare IT, NABH implementation and digital marketing.

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

July 17, 2018 / AI/ML/DL / 6 minutes of reading

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

July 6, 2018 / AI/ML/DL / 6 minutes of reading

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

mfine, the MVP of a “Cloud Clinic” by Prasad Kompalli, @pkompalli CEO & cofounder, mfine @mfinecare

June 27, 2018 / AI/ML/DL, TELEMEDICINE / 4 minutes of reading

It has been roughly a year since Ashu and I started mfine. During the course of this journey, we studied the healthcare sector very closely and started interacting with people in the ecosystem. The more we learnt, the more we got passionate about healthcare and at times obsessed with it!

…

mfine, the MVP of a “Cloud Clinic” by Prasad Kompalli, @pkompalli CEO & cofounder, mfine @mfinecareRead More »

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

February 26, 2018 / AI/ML/DL / 5 minutes of reading

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.

Author

[tab]
[content title=”About Manishree Bhattacharya” icon=”fa-heart”]

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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Glossary of Terms & Acronyms for Artificial Intelligence and Machine Learning

February 9, 2018 / AI/ML/DL / 7 minutes of reading

AI & Machine Learning Terms

Artificial intelligence The development of computers capable of tasks that typically require human intelligence. A machine’s ability to make decisions and perform tasks that simulate human intelligence and behavior.

Machine learning Using example data or experience to refine how computers make predictions or perform a task. A facet of AI that focuses on algorithms, allowing machines to learn without being programmed and change when exposed to new data. 

Deep learning A machine learning technique in which data is filtered through self-adjusting networks of math loosely inspired by neurons in the brain. The ability for machines to autonomously mimic human thought patterns through artificial neural networks composed of cascading layers of information.

Supervised learning Showing software labeled example data, such as photographs, to teach a computer what to do. A type of machine learning in which output datasets train the machine to generate the desired algorithms, like a teacher supervising a student; more common than unsupervised learning.

Unsupervised learning Learning without annotated examples, just from experience of data or the world—trivial for humans but not generally practical for machines. Yet. A type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. The most common unsupervised learning method is cluster analysis.

Reinforcement learning Software that experiments with different actions to figure out how to maximize a virtual reward, such as scoring points in a game.

Artificial general intelligence As yet nonexistent software that displays a humanlike ability to adapt to different environments and tasks, and transfer knowledge between them.

Large-scale Machine Learning Design of learning algorithms, as well as scaling existing algorithms, to work with extremely large data sets.

Deep Learning Model composed of inputs such as image or audio and several hidden layers of sub-models that serve as input for the next layer and ultimately an output or activation function.

Natural Language Processing (NLP) Algorithms that process human language input and convert it into understandable representations. The ability for a program to recognize human communication as it is meant to be understood. 

Collaborative Systems Models and algorithms to help develop autonomous systems that can work collaboratively with other systems and with humans.

Computer Vision (Image Analytics) The process of pulling relevant information from an image or sets of images for advanced classification and analysis.

Algorithmic Game Theory and Computational Social Choice Systems that address the economic and social computing dimensions of AI, such as how systems can handle potentially misaligned incentives, including self-interested human participants or firms and the automated AI-based agents representing them.

Soft Robotics (Robotic Process Automation – RPA) Automation of repetitive tasks and common processes such as IT, customer servicing and sales without the need to transform existing IT system maps.

Algorithms: A set of rules or instructions given to an AI, neural network, or other machines to help it learn on its own; classification, clustering, recommendation, and regression are four of the most popular types.

Artificial neural network (ANN): A learning model created to act like a human brain that solves tasks that are too difficult for traditional computer systems to solve.

Autonomic computing: A system’s capacity for adaptive self-management of its own resources for high-level computing functions without user input.

Chatbots: A chat robot (chatbot for short) that is designed to simulate a conversation with human users by communicating through text chats, voice commands, or both. They are a commonly used interface for computer programs that include AI capabilities.

Classification: Classification algorithms let machines assign a category to a data point based on training data.

Cluster analysis: A type of unsupervised learning used for exploratory data analysis to find hidden patterns or grouping in data; clusters are modeled with a measure of similarity defined by metrics such as Euclidean or probabilistic distance.

Clustering: Clustering algorithms let machines group data points or items into groups with similar characteristics.

Cognitive computing: A computerized model that mimics the way the human brain thinks. It involves self-learning through the use of data mining, natural language processing, and pattern recognition.

Convolutional neural network (CNN): A type of neural networks that identifies and makes sense of images.

Data mining: The examination of data sets to discover and mine patterns from that data that can be of further use.

Data science: An interdisciplinary field that combines scientific methods, systems, and processes from statistics, information science, and computer science to provide insight into phenomenon via either structured or unstructured data.

Decision tree: A tree and branch-based model used to map decisions and their possible consequences, similar to a flow chart.

Fluent: A type of condition that can change over time.

Game AI: A form of AI specific to gaming that uses an algorithm to replace randomness. It is a computational behavior used in non-player characters to generate human-like intelligence and reaction-based actions taken by the player.

Genetic algorithm: An evolutionary algorithm based on principles of genetics and natural selection that is used to find optimal or near-optimal solutions to difficult problems that would otherwise take decades to solve.

Heuristic search techniques: Support that narrows down the search for optimal solutions for a problem by eliminating options that are incorrect.

Knowledge engineering: Focuses on building knowledge-based systems, including all of the scientific, technical, and social aspects of it.

Logic programming: A type of programming paradigm in which computation is carried out based on the knowledge repository of facts and rules; LISP and Prolog are two logic programming languages used for AI programming.

Machine intelligence: An umbrella term that encompasses machine learning, deep learning, and classical learning algorithms.

Machine perception: The ability for a system to receive and interpret data from the outside world similarly to how humans use our senses. This is typically done with attached hardware, though software is also usable.

Recurrent neural network (RNN): A type of neural network that makes sense of sequential information and recognizes patterns, and creates outputs based on those calculations.

Swarm behavior: From the perspective of the mathematical modeler, it is an emergent behavior arising from simple rules that are followed by individuals and does not involve any central coordination.

Terminology	Definition
Automated communications	Also known as an interactive agent, or artificial conversational entity, these are computer programs which conduct a conversation via auditory or textual methods. For example, chatbots, mailbots.
Automated data analyst	AI solutions aimed at performing the job of data analysts and data scientists and bridging the gap between such roles and business imperatives. For example, these might include programs that are able to develop a deep understanding of customer preferences from data, identify high-risk customer groups and tailor interaction touch points in a manner personalised to such customers.
Automated operational and efficiency analyst	AI solutions targeted at increasing operational efficiency and reducing costs. These include AI programs and bots aimed at automating repetitive manual tasks such as identifying and correcting data and formatting mistakes, performing back office tasks and automating repetitive interactions with customers.
Automated research and information aggregation	Applications of AI that involve aggregating and processing large volumes of information on a topic so as to generate meaningful insights. For example, aggregating information from research papers or medical journals for diagnosis support, identifying online hoax, bad reporting and statistics, and identifying plagiarised publications.
Automated sales analyst	AI-powered digital analysts for sales and marketing decisions. These programs are able to test a range of scenarios using internal and external data to predict the impact of marketing strategies such as promotions and campaigns, simulate ‘what if’ scenarios against multiple hypotheses and perform root cause analyses against business results.
Business decision makers/influencers	A sub-set of participants in the survey who have identified themselves to be either in a decision making role or an influencing role in their current organisations. Some of the survey questions had been specifically targeted towards this group.
Decision support systems	Decision support systems (DSS) are a specific class of computerised information systems that support business and organisational decision-making activities.
Machine learning	Machine learning is concerned with computer programs that automatically improve their performance through experience.
Predictive analytics	Predictive analytics is an area of statistics that deals with extracting information from data and using it to predict trends and behaviour patterns–for example, sales forecasts, predicting customer churn and industrial machine failure.
Robotics	Robotics deals with the design, construction, operation and use of robots, as well as computer systems for their control, sensory feedback and information processing. Environmental information such as imagery and sound are captured using a group of sensors and the same are processed using various computerised techniques for the robot to respond.
Virtual personal assistants	Virtual assistants use natural language processing (NLP) to match user text or voice input to executable commands. Many continually learn using AI techniques, including machine learning. For example, Apple’s Siri, Amazon’s Alexa, Google Now.
AI advisors	AI advisors are machines or systems that monitor employees’ progress and performance. They are responsible for the growth of the employee in the organisation and for the delivery of projects.
AI assistants	AI assistants are machines or systems or application programming interfaces ([APIs] a set of subroutine definitions, protocols and tools for building application software) that perform non-value adding services such as scheduling and email management.

Source: https://www.pwc.in/assets/pdfs/consulting/technology/data-and-analytics/artificial-intelligence-in-india-hype-or-reality/artificial-intelligence-in-india-hype-or-reality.pdf

References

[1]: pwc AI: https://www.pwc.com/ai

[2]: AI: The Complete Guide:
https://www.wired.com/story/guide-artificial-intelligence/
[3]: https://dzone.com/articles/ai-glossary

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

December 22, 2017 / AI/ML/DL / 3 minutes of reading

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

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

August 13, 2016 / AI/ML/DL / 4 minutes of reading

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

July 14, 2016 / AI/ML/DL / 5 minutes of reading

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

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

Author

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Manish Sharma

Founder HCITExpert.com, Digital Health Entrepreneur

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email | manish.sharma@hcitexpert.com

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