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Indian Healthcare Is All Set To Be Transformed By AI

The integration of AI in healthcare in India has been seen as a key technology toward improving the efficiency, quality, cost, and reach of healthcare and is being promoted by stakeholders, industry bodies, and the government.

Artificial intelligence (AI) has already started making inroads into various industries. Healthcare is emerging as one of the biggest beneficiaries of the AI revolution. The emerging use cases of AI in the healthcare sector can be seen as a collection of technologies enabling machines to sense, comprehend, act, and learn so they can perform administrative and clinical healthcare functions, as well as be used in research and for training purposes. Unlike legacy technologies that only complemented human skills, health AI today can significantly expand the scope of human activity. These technologies include, among others, natural language processing, intelligent agents, computer vision, machine learning, expert systems, chatbots, and voice recognition. These technologies can also potentially be used to compensate for a physician’s cognitive biases. This use and adoption of AI can be seen at varying levels across the healthcare ecosystem. AI addresses the issue of information overload often faced in healthcare by employing machine learning to make sense of otherwise overwhelming volumes of healthcare data, which can otherwise threaten the adoption of evidence-based practice. Programs such as IBM’s Watson for Oncology extensively evaluate medical literature to prescribe the best course of treatment. Researchers have used smart algorithms to extract information from radiology reports contained in a repository spanning multiple institutions. Theoretically, AI can use a person’s genome to recommend the most effective treatment option with the least side effects.

India is also joining a growing list of the countries that are using AI in the healthcare. The adoption of AI in India is increasing with new startups and large ICT companies offering AI solutions for healthcare challenges in the country. Such challenges and solutions include addressing the uneven ratio of skilled doctors to patients and making doctors more efficient at their jobs; the delivery of personalized healthcare and high-quality healthcare to rural areas; and training doctors and nurses in complex procedures. Companies are offering a range of solutions including automation of medical diagnosis, automated analysis of medical tests, detection and screening of diseases, wearable sensor-based medical devices and monitoring equipment, patient management systems, predictive healthcare diagnosis, and disease prevention. In developing these solutions, a commonly cited challenge has been the lack of a comprehensive, representative, interoperable, and clean data — something that is intended to be addressed through the Electronic Health Records Standards developed by the Ministry of Health and Family Welfare. Other challenges include access to open medical data sets and adoption by practitioners.

According to a report by CIS India published earlier in 2018, AI could help add USD 957 billion to the Indian economy by 2035. Among several companies that are exploring various uses of AI in the healthcare segment, Microsoft is taking a major initiative along with Apollo and other hospitals to expand its use in several segments like cardiology, eye-care, and diseases like tuberculosis, and HIV. Microsoft has announced a partnership with Apollo Hospitals to build an AI-centric cardiology network. The company will use AI models for predicting heart disease risks in patients and support physicians with targeted treatment plans. Siemens Heathineers has leveraged AI to develop a digital twin heart that mimics the electrical and physical properties of real cardiac cells enabling surgeons to run simulations before surgery, to see if a pacemaker is appropriate for a particular patient, for example. Philips sells AI-enabled heart models that convert two-dimensional ultrasound images into data that helps physicians diagnose problems or automatically analyze scans to help surgeons plan operations. Google has developed a portfolio of AI tools, including algorithms to analyze medical images to diagnose eye disease and to sort through medical records to predict patient outcomes. Max Healthcare, one of the leading hospital chains in North India, is deploying AI to monitor critical care. According to the company, the technology has already brought down critical care costs by about 30 percent along with optimized use of ICU beds.

According to a report by CIS India published earlier in 2018, AI could help add USD 957 billion to the Indian economy by 2035.

Government initiatives
The integration of AI in healthcare in India has been seen as a key technology toward improving the efficiency, quality, cost, and reach of healthcare and is being promoted by stakeholders, industry bodies, and the government. In India, assistive AI enjoys the most potential for growth while technologies that have the potential to replace doctors have the least chances of succeeding, one of the reasons being a conflict of interest among the medical establishment. The focus of most AI-based healthcare initiatives has been to extend medical services to traditionally underserved populations such as in rural areas that do not have the required infrastructure or enough primary physicians, and economically weaker sections of society who may not be able to afford certain medical facilities. Therefore, AI, as it is used in healthcare in India, appears to be addressing issues of economic disparity rather than widening existing gaps as feared.

NITI Aayog is working on early diagnosis and detection of diabetic retinopathy and cardiac risk based on the AI models. Such initiatives would in the long run help patients on proactive medication in early stages rather than reactive healthcare in advanced stages – bringing down healthcare costs and better chances of recovery. Aiming to foster growth for India’s nascent AI and machine learning ecosystem, NITI Aayog and Google have come together to work on a range of initiatives to help build the AI ecosystem across the country. NITI’s partnership with Google will unlock massive training initiatives, support startups and encourage AI research through Ph.D. scholarships, all of which contributes to the larger idea of a technologically-empowered new India. Google through the NITI Aayog, will conduct hands-on training programs that aim to sensitize policymakers and technical experts in governments about relevant AI tools, and how they can be used to streamline governance.

NITI Aayog has been entrusted to set up a national program to conduct research and development in frontier technologies such as AI. In furtherance of this mandate, NITI Aayog has been developing India’s national strategy on AI along with the national data and analytics portal to enable the wide deployment and use of AI. On June 4, 2018, NITI Aayog published a discussion paper titled National Strategy for Artificial Intelligence. While recognizing the potential of AI for the economic and social growth of India, the paper identifies five sectors which are set to play a pivotal role in the adoption of AI in the country and are likely to benefit from government intervention – healthcare, agriculture, education, smart cities and infrastructure, and smart mobility and transportation. The paper advocates the use of robotics and Internet of Medical Things for solving problems in healthcare in India and helping the government meet its social objectives. Considering the low affordability and penetration of health services in India, it does not visit details of how such a task will be implemented or scaled up.

India today is witnessing significant trends in health, an increasing prevalence of non-communicable diseases for instance, as well as marked demographic shifts. Climbing out-of-pocket costs is becoming difficult for most households. The National Health Stack (NHS) envisages a centralized health record for all citizens of the country in order to streamline the health information and facilitate effective management of the same. The proposed NHS is an approach to address the challenge and seeks to employ the latest technology including big data analytics and machine learning AI, a state-of-the-art policy mark-up language and create a unified health identity of citizens – as they navigate across services across levels of care, i.e. primary, secondary, and tertiary and also across public and private. The health ministry is also in process of setting up a National Digital Health Authority, a statutory body for creating frameworks, regulations, and guidelines for interoperability and exchange of digital information. Cybersecurity and protection of privacy of patient health data are major areas where cooperation from various countries would be required. This area also requires collaboration with industry and academia to come up with sustainable strategies to fight cybercrime. Similar collaborations would also be required when the government intends to use AI and machine learning in hospitals or in community settings for use by health workers.

State governments are also providing support to AI startups. For instance, the Karnataka government is mobilizing Rs 2000 crore by 2020 toward supporting the same. The Karnataka government also has a startup policy and Karnataka Information Technology Venture Capital Fund that can support AI startups. Moreover, in February 2018, National Association of Software & Services Companies (NASSCOM) signed a MoU with the Telangana government to establish a Centre of Excellence for Data Science and Artificial Intelligence. To be established in a public-private partnership mode, the CoE is the latest addition to Nasscom’s hub-and-spoke network of CoEs across major locations in the country, aimed at evangelizing new and emerging technologies, the computer industry’s apex body said. With an initial joint investment of Rs 40 crore, the center will catalyze the growth of the deep tech ecosystem in Telangana, by providing the stimulus for innovation and in-depth research in the areas of data science and AI. It will provide high-end technology, field expertise, and curated programs to augment capabilities across academia, enterprises, government, innovators and advanced start-ups.

The government is also partnering up with other countries to increase the scope of AI in healthcare. Under the UK-India Technology Partnership, Indian Prime Minister, Narendra Modi and UK Prime Minister, Theresa May in April 2018 discussed strategic partnership and growing convergence on regional and international issues. Both sides will scale up collaboration on future tech to tackle global challenges; realizing the potential of AI; the digital economy; health technologies; cybersecurity; and promoting clean growth, smart urbanization and future mobility – while developing the future skills and capabilities of our youth. The Government of India welcomed the UK initiative of establishing a UK-India tech hub in India as a part of the growing bilateral Technology Partnership. The tech hub will bring together hi-tech companies to create investment and export opportunities and provide a new platform to share the very best technologies and advance policy collaboration including on future mobility, advanced manufacturing and healthcare AI under India’s aspirational districts’ program. The government will establish a series of new partnerships between UK regional and Indian state-level tech clusters, to drive joint innovation and R&D.

The proposed NHS is an approach to address the challenge and seeks to employ the latest technology including big data analytics and machine learning AI.

Startups transforming healthcare sector in India
The health sector in India has seen the rise of a number of startups who have taken it upon themselves to bring innovations in sub-sectors such as AI in the health industry, mHealth (which is perhaps the largest application of digital healthcare), telemedicine, patient data storage as well as remote diagnosis. Companies offering services based upon technology, commercial models as well as data analytics are on the rise with a view to effecting positive change in the Indian health sector. This has been further boosted by the government’s policy which seeks to encourage startups. Based upon a joint study by the Federation of Indian Chambers of Commerce and Industry (FICCI) as well as KPMG, about 8 percent of monetary investments in the business to consumer (B2C) startups in India find their roots in the healthcare sector. There has been an influx of investment as far as the application of AI in the health sector is concerned in India. These startups offer the customers services that range from the detection of cancer to the process involved in finding a new healthcare provider, although, this is mainly recognized in the urban areas, its application appears to be gradually gaining inroads into the rural settlements.

One such company is Tricog Health, a startup handpicked by GE’s healthcare accelerator program for its cloud-based cardiac diagnosis platform. Tricog increases access to cardiac care across 340 cities in 23 states, including in some of the most remote locations in India. The company’s platform collects physiological data and ECGs from medical devices in the field and then uses specialized AI to process the data in real time and give the cardiologist a diagnosis. Another startup, Bengaluru-based Aindra Systems, is using AI to tackle cervical cancer, which is the second most common cancer among Indian women between the ages of 15 and 60. Aindra’s solution can detect cervical cancer in its early stages and measurably increase the odds of survival. The company increases the productivity of the pathologists screening cervical cancer samples, who otherwise typically need to manually examine each sample and flag cases with a high cancer probability to an oncologist for further review. HealthifyMe is working on lifestyle diseases like obesity, hypertension, and diabetes. With its AI-enabled nutrition coach, Ria, HealthifyMe brings the best of elite nutrition expertise with AI in the loop. MUrgency, a Mumbai-based healthcare mobile application is helping people connect in need of medical emergency responses with qualified medical, safety, rescue, and assistance professionals.

The AI boom in healthcare is just starting, and the up-and-coming list of players is endless. Niramai is working on early detection of breast cancer. Ten3T is providing remote health monitoring services via AI to detect anomalies and alert the patient’s doctor. Advancells provides stem cell therapy, also known as regenerative therapy, has a large potential in the field of organ transplantation. Portea offers home visits from doctors, nurses, physiotherapists and technicians for patients. Patients who are unable to visit hospitals can receive assistance from doctors and medical professionals using remote diagnostics and monitoring equipment, point-of-care devices. AddressHealth provides primary pediatric healthcare services to school children where they are screened for hearing, vision, dental health, anthropometry, alongside a medical competition. LiveHealth works as a management information system (MIS) for healthcare providers. It collects samples, manages patient records, diagnoses them and generates reports. AI diagnostics startup SigTuple is reducing the burden of pathologists with intelligent digital analysis of blood samples. Another 3D printing startup Supercraft is developing AI visualization tools that equip doctors, hospitals, and researchers with deeper insights into human anatomy.

These startup-driven innovations and global platforms are just the tip of the iceberg. AI can ultimately become a force multiplier in bringing preventative healthcare to everyone, rather than just those in urban or affluent communities. As AI experts often say, more data beats better algorithms, in other words, simpler algorithms only need a larger training dataset to generate accurate, valuable predictions for both payers and providers. With 1.3 billion citizens, India has the potential to provide the vast amounts of data needed to improve the accuracy and precision of these algorithms and empower both startups and large companies to help solve healthcare problems around the world.

Challenges to AI in India
While advancements in AI are India’s best bet yet to sustain its crumbling healthcare infrastructure, visionless implementation of AI medical technology and the absence of a robust legal framework can only compound the crisis, and not mitigate it. There are a number of challenges to the implementation and adoption of AI in India including:

Regulatory authority. At present, India lacks a regulating authority for AI in healthcare. Possible options include the Medical Council of India (National Medical Commission), the Drug Controller General of India, or a new entity established specifically for this area. A possible alternative could be empowering the MCI to oversee the medical aspects, and a regulator under the Data Protection Bill to oversee issues relating to data. There is also a regulatory gap around medical devices, which has sought to be addressed by the recent Indian Medical Devices Rules, 2017.

Appropriate certification mechanism. One of the biggest issues with the adoption of AI in healthcare in India is acceptability of results, which include direct results arrived at using AI technologies as well as opinions provided by medical practitioners that are influenced/aided by AI technologies. Start-ups in the field often find that they are asked to show proof of a clinical trial when presenting their products to doctors and hospitals, yet clinical trials are expensive, time consuming and inappropriate forms of certification for medical devices and digital health platforms. Startups also face difficulty in conducting clinical trials and as there is a lack of a clear regulation to adhere to. They believe that while clinical trials are a necessity with respect to drugs, the process often results in obsolescence of the technology by the time it is approved in the context of AI. Yet, medical practitioners are less trusting towards startups who do not have approval from a national or international authority. A possible and partial solution suggested by these startups is to enable doctors to partner with them to conduct clinical trials together.

Infrastructure. Though India is working to develop and improve national infrastructure necessary for AI to take off in the country, it remains ignored by policymakers. Cloud computing infrastructure, for example, is mostly concentrated in servers outside India. Delays in investing in native infrastructure have resulted in many Indian startups incorporating themselves outside India due to easier access to infrastructure and technology.

Investment. Investment, though growing, in health-related AI in India appears to be currently limited and research is under-funded and explored, especially by the government.

Information asymmetries and perceptions. AI-based healthcare solutions often face the issue of information asymmetry between the doctors who use the system and the coders who built it. This may result in hesitation in adopting the software. Furthermore, the perception of AI technologies can be a direct causative factor on how effectively they can be used in treatment. This needs to be researched more, especially in the context of developing countries like India, where the penetration and understanding of technologies are significantly different than in the developed world.

Unanswered legal questions. How do AI programs ensure patient consent and privacy of sensitive medical data? How to address the questions of apportionment of liability among the practitioner, hospital, and the AI system developer, trainer, and manager in case of an act of medical negligence? In the event of an AI diagnostic error, or inaccurate use of data, or a technological malfunction, who would be held liable: the practitioner, the AI developer, the specific program engineer who designed it or the AI robot (if the robot used its intelligence to make its own decision)? How to determine the degree of accountability of the operating physician when a wrong diagnosis or treatment occurs due to an error in the primary data feed or an AI systemic glitch? And so on.

Global market
The global AI in healthcare market was valued at USD 3187 million in 2018 and is estimated to reach USD 22,790 million by 2023, registering a CAGR of 48.7 percent from 2018 to 2023, estimates Allied Market Research. The growth of the market is driven by the ability of AI to improve patient outcomes, need to increase coordination between healthcare workforce and patients, increase in adoption of precision medicine, and a notable rise in venture capital investments. In addition, the rise in importance of big data in healthcare is expected to fuel the market growth. Furthermore, technological advancements in AI systems are anticipated to augment  market growth. Prospective of AI-based tools for elderly care and the untapped potential of emerging markets, such as China and India, are expected to present various opportunities for market expansion. However, an imprecise regulatory scenario and reluctance among healthcare professionals to adopt AI-based technologies are expected to hamper the growth.

AI systems are mainly categorized into machine learning techniques and natural language processing. Machine learning techniques involve the analysis of structured data such as genetic data, and imaging data, whereas, natural language processing involves extracting information from unstructured data such as clinical notes, and medical journals. Microsoft Corporation and Adaptive Biotechnologies partnered to combine AI with human immune system sequencing (from Adaptive Biotechnologies) in January 2018. The University of Massachusetts Amherst Center for Data Science partnered with Chan Zuckerberg Initiative (CZI) in January 2018 to accelerate healthcare research and development using AI. Furthermore, January 2018, Wilfrid Laurier University researchers and health researchers in Waterloo University collaborated to study the use of AI for early detection of Alzheimer’s. Owkin France raised USD 11 million in January 2018, to develop artificial algorithms for speeding up the drug development process. Biotricity, Inc. —a medical diagnostic and consumer healthcare technology company —is expanding research and development, to include AI into its product offerings. Furthermore, Ubenwa — a Nigerian AI health startup — founded in 2014 —developed a mobile app to detect asphyxia in newborn babies using machine learning. 

Key players in the market are focusing on collaborations in order to expand its presence in the market. For instance, in 2017, Nvidia Corporation collaborated with GE Healthcare to speed up the adoption of AI in healthcare. As per the agreement, Nvidia Corporation is expected to integrate its AI to GE Healthcare’s 500,000 imaging devices, globally. Some major players operating in the global market are IBM Corporation, Google, Nvidia Corporation, Microsoft Corporation, iCarbonX, Next IT Corporation, CloudMex, Carescore, Atomwise, Zephyr Health, Siemens Healthineers, Deep Genomics, Welltok, Intel, Medtronic, Koninkiljke Philips, and Oncora Medical.

In addition to bigger companies such as Google and IBM, India is also host to startup companies that specialize in harnessing AI to diagnose disease.

AI and healthcare segments in India
Hospitals. Hospitals in India are employing descriptive and predictive AI. For instance, the Manipal Group of Hospitals has tied up with IBM’s Watson for Oncology to aid doctors in the diagnosis and treatment of 7 types of cancer. Watson for Oncology is used across its facilities, where more than 200,000 patients receive cancer care each year. Here, AI is used to analyze data and research evidence and improve the quality of the report, in turn increasing patient trust. Importantly, patients are fully aware of the process and provide their express consent. Due care is also taken to preserve patient anonymity. Aravind Eye Care Systems is presently working with Google Brain, after previously helping Google develop its retinal screening system by contributing images to train its image parsing algorithms. After successful clinical trials to detect signs of diabetes-related eye disease, it is now attempting to put it to routine use with patients. Products such as Microsoft Azure, machine learning, data analytics, CRM online, and Office 365 are being used by private healthcare providers such as Fortis Healthcare, Apollo Hospitals, LV Prasad Eye Institute (LVPEI), Narayana Health, and Max Healthcare to improve patient care.

Pharmaceuticals. Streamlining the process of drug discovery, the application of AI in pharma offers additional advantages such as identification of both tangible and intangible enhanced value proposition, enhanced competitor differentiation, optimal resource allocation for maximum market share gain, revenue and profit, ability to maximize growth, customizing sales and marketing messaging for greater customer engagement, and automation of sales and marketing messages and channels. Abbott Healthcare has used India as a testing ground for new tech innovations such as apps for the heart and liver, as well as vertigo exercises (which use augmented and virtual reality). Pharmarack is a software-as-a-service (SaaS) based application that utilizes AI to automate the pharmaceutical supply chain management.

Diagnostics. In addition to bigger companies such as Google and IBM, India is also host to startup companies that specialize in harnessing AI to diagnose disease. From a review of solutions adopted it appears that diagnostics in India are employing descriptive and predictive AI. For example, Niramai Health Analytix uses thermal analytics for early-stage breast cancer detection, while Advenio Tecnosys detects TB from chest X-rays and acute infections from ultrasound images. Qure.Ai uses deep learning technology to help diagnose the disease as well as recommend personalized treatment plans from healthcare imaging data, and Orbuculum uses AI to predict diseases such as cancer, diabetes, neurological disorders, and cardiovascular diseases through genomic data. Cureskin diagnoses six types of common skin conditions – pimples, acne, scars, dark spots, pigmentation, and dark circles – and recommends treatment regimens through a mobile app.

According to the WHO, India is home to over five crore Indians suffering from depression and is a major contributor to global suicides. However, seeking help for mental health issues is still stigmatized. In India, AI is being employed through chatbots such as Wysa that provide mental health support. A person can chat anonymously with an AI-enabled system, and the chatbot is intended to provide empathetic support and suggest practitioners to consult. This could encourage people to open up without hesitation. One such example is the outlet provided by such apps for displaced workers from India’s IT industry to vent their fears about potential job losses in the upheaval facing the industry at present. Woebot is a similar service that tracks changes in user mood on a weekly basis and finds patterns within them, offering techniques to deal with these issues.

Telemedicine. From a review of solutions adopted it appears that companies developing telemedicine platforms in India are employing descriptive and predictive AI. However, telemedicine currently faces infrastructural challenges and is dependent on the quality of services provided by the medical professional. By doing away with the human component, AI standardizes the quality of care. SigTuple can analyze blood slides and generate a pathology report without assistance from a pathologist. This service can be utilized in remote areas at a fraction of what it would usually cost. Microsoft has teamed up with the Government of Telangana to use cloud-based analytics for the Rashtriya Bal Swasthya Karyakram program by adopting MINE (Microsoft Intelligent Network for Eyecare), an AI platform to reduce avoidable blindness in children. The Philips Innovation Campus (PIC) in Bengaluru is harnessing technology to make healthcare affordable and accessible. They have developed solutions for TB detection from chest x-rays, and a software solution (Mobile Obstetrics Monitoring) to identify and manage high-risk pregnancies. It has partnered with Fortis Escorts Heart Institute, Delhi to set up Philips IntelliSpace Consultative Critical Care, where hospitals can now monitor multiple intensive care units (ICUs) from a central command center that may be located in a geographically-separated area.

Road ahead
AI is going to disrupt the way business is done and India, in particular, is uniquely poised in utilizing AI to innovate for social and inclusive good. With its vast inequalities in healthcare distribution, glaring lack of trained healthcare clinicians and infrastructure, and low government spending on healthcare, India is one of the countries in the world with the most room for innovative, sustainable and scalable healthcare technology to improve lives. By performing descriptive, predictive, and prescriptive functions, AI in healthcare in India is currently augmenting human capacity rather than to replacing human labor altogether. India presently is in a unique position to be a driver in the AI and healthcare space for national and international companies. With large amounts of data and a burgeoning startup community, India has the opportunity to address many healthcare related problems through the use of AI.

In its quest for India to join the AI revolution, the government has also undertaken a number of initiatives to drive the adoption of AI across the country. Yet, many barriers still stand in the way of widespread adoption and implementation, arising out of a lack of regulatory clarity on issues of data, design and certification and lack of resilient and ethical data collection and processing systems. A robust open data policy, a comprehensive privacy legislation, greater investment in AI research and development, robust national infrastructure, equipping labor forces with the necessary skills to adopt AI and to be prepared for the changes that AI could bring, and a regulatory framework that ensures transparency and accountability but does not hinder innovation, are some of the measures required for the establishment of a flourishing AI healthcare ecosystem in India.

However, government enthusiasm for innovation and locally made technology is at an all-time high – both at the central policy level, as well as at local level, with individual states seeking to outdo each other at the adoption of new technology that can help solve old problems. Support for public-private partnerships is also high. While this must be tempered by a healthy dose of skepticism about real ground conditions, this is an encouraging sign for more innovators!

Companies developing telemedicine platforms in India are employing descriptive and predictive AI.

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