Girish Bommakanti and Dr Vinisha Poojari


“Artificial Intelligence” is among the most frequently used buzzwords in the tech world today. The term artificial intelligence was coined by Mr. John McCarthy in 1956. Artificial intelligence has been attracting huge investments owing to its ability to disrupt many areas of work where human effort is required. Many renowned companies are investing in building and integrating AI algorithms for business development. There has been significant growth in the number of companies filing for AI technology patents since 2010.

Recently at the G20 summit, most of the early adopters of AI systems showcased their work towards building the “Artificial intelligence quotient” (AIQ). On the other hand, though India has still not explored the full-length potential of AI and its applications, it remains the most competitive among the countries in South Asia. To recognize the full opportunities that Artificial Intelligence offers, Indian policymakers, universities, researchers, entrepreneurs, and corporations need to come together and work towards the adoption of artificial intelligence.

Introduction to Artificial Intelligence

The word “artificial intelligence” is used to describe machines that mimic “cognitive functions” that humans associate with other human minds such as “learning” and “problem-solving”. It is a cluster of technologies that involves replicating human intellectual processes through machines, especially computers. AI system works by merging with large intelligent, iterative processing algorithms. This combination makes the AI understand the patterns and features in the analysed data.

Figure 1- Evolution of Artificial Intelligence

Artificial intelligence has various constellations of technology, among them being machine learning applications such as pattern recognition, and signal processing. This machine learning application is further categorized, based on learnings from data, into supervised learning, unsupervised learning, and reinforced learning. One more type from the family of machine learning is deep learning usually used for large data sets.

Artificial intelligence systems are divided based on capabilities and functions.

Figure 2- Types of Artificial Intelligence


Brief description of types of Artificial intelligence (AI)

Table 1 – Based on capabilities – There are three types which have been explained below –

Table 2 – Based on Functionality – The AI systems have been divided into four major areas based on the functionality of the algorithms

Global AI technology portfolio and policy

AI has enormous potential to give a boost to the economy as its applications can have a significant impact in areas like education, healthcare, and business development in the near future.

Many countries have instituted dedicated public offices such as the Ministry of AI (UAE), and Office of AI and AI Council (U.K.) while China and Japan have allowed existing ministries to take up AI implementation in their sectoral areas. Governments globally are reviewing and developing their position in the following areas to rapidly grow AI ecosystems:

Table – 3- Funding commitments made by governments across the world to promote AI research and application.

India’s effort towards AI development and adoption

India needs to develop a framework that will help the country leverage the full potential of AI technologies.

The framework can comprise three interrelated areas –

a) Opportunity: the economic impact of AI on India

b) AI for Greater Good: social development and growth

c) AI Garage for 40 percent of the world: solution provider of choice for emerging and developing economies across the globe

  1. Potential impact of AI on the Indian economy

AI is emerging as a new solution to overcome the physical limitations of capital and labour

and to open new sources for growth. AI can drive growth through a) the ability to automate complex physical tasks that require adaptability and agility across industries, (b) enabling humans to focus on parts of their role that add the most value, and improve capital efficiency, and (c) innovation. Accenture’s latest 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.

  • AI for Greater Good: Social development and growth

AI has the potential to not only contribute towards the growth of the economy but also towards improving social aspects of development such as the quality of life, access to care, building smart cities and developing financial inclusion for sections of people who are excluded from the regular government offerings.

  • AI Garage for 40% of the world: solution provider of choice for emerging and developing economies across the globe.

India can become a preferred choice for various enterprises and institutions to try various AI solutions which can be scaled up later to other developing and emerging economies.AI solutions can be developed in the fields of healthcare, education, and agriculture for addressing the effects of climate change.  For example, an advanced AI-based solution for early diagnosis of tuberculosis (one of the top 10 causes of death worldwide) could easily be rolled out to countries in South and Southeast Asia or Africa, once developed and refined in India. Similarly, AI-based solutions for breast cancer (the most common cancer among women) can be screened and diagnosed at a much earlier stage, allowing for the timely treatment of patients.

Accelerating the Adoption of AI in India

Wipro has built Holmes, an AI platform that deploys “bots” to carry out repetitive and mundane tasks. TCS has created its own AI platform, Ignio, and Infosys has built Nia, improving upon its earlier Mano platform. The success of the Indian players in adopting and promoting AI is limited. It would require support from the government and market segments where it can be implemented. For example, the Ayushman Bharat Digital Mission will boost the AI market adoption in healthcare, thereby creating a universal data set of patient records. This will help the policymakers in designing a better healthcare system with accessibility solutions for the overall population.

WHO on Artificial Intelligence for Health

WHO has released a set of recommendations on governance, ethics, and rights while incorporating AI technology into the healthcare ecosystem. These are:

  1. Governments should support the global governance of AI for health to ensure that the development and amalgamation of AI technologies abide by the full spectrum of ethical norms, human rights protection, and legal obligations.
  2. Global health bodies such as WHO, Gavi, the Vaccines Alliance, the Global Fund to Fight AIDS, Tuberculosis and Malaria, Unitaid and major foundations should commit themselves to ensure that adherence to human rights obligations, legal safeguards and ethical standards is a core obligation of all strategies and guidance.
  3. International agencies, such as the Council of Europe, OECD, UNESCO and WHO, should develop a common plan to address the ethical challenges and the opportunities of using AI for health, for example through the United Nations Interagency Committee on Bioethics.
  4. Governments and international agencies should engage nongovernmental and community organizations, particularly for marginalized groups, to provide diverse insights.
  5. Civil society should participate in the design and use of AI technologies for health as early as possible in their conceptualization.

An example of an effective AI tool was Florence developed by WHO during COVID-19. Later its application was extended to create awareness about the ill effects of tobacco consumption. Florence is a digital health care assistant, as part of their AI for the Quitting Tobacco initiative. Using computer-generated imagery, animation and AI, Florence is designed to help people quit tobacco and additionally combat misinformation about COVID-19, through online, face-to-face conversation.

Artificial Intelligence can help to inform the right intervention to the right people at the right time. Interactive online tools or apps powered by AI technologies can provide access to individual risk assessment and risk reduction recommendations which are more engaging than traditional approaches, especially in lifestyle disorders or mental health issues.

Applications of Artificial Intelligence

Existing and prospective applications of AI technologies catering to the field of public health in India.

Healthcare is a challenging sector in India and is expected to grow to USD280 billion by 2020.Artificial Intelligence has great potential to contribute towards improving public health and thereby promote the health of all people, especially in resource constraint settings like India. India’s public health ecosystem provides service delivery through various national programs for immunization, communicable and non-communicable diseases, disease surveillance and management of the population’s health. Rapid growth in accessing health-related data and advances in data storage has brought opportunities for Artificial intelligence (AI) in the field of public health.

The four major challenges posed by the public healthcare system in India.

  1. Shortage of qualified health professionals like doctors, nurses
  2. Accessibility to healthcare services – a huge disparity between rural and urban India.
  3. Lack of affordability leading to high out of pocket expenditure
  4. Lack of awareness about the diseases and where to access the right treatment

There are a few areas within this ecosystem where AI technologies can be of great support. These are a) Data governance b) Data Analytic infrastructure and processing c) Addressing the skills gaps in the workforce.

The availability of data relevant to health has drastically increased, providing insights into social, behavioral, and environmental determinants of health. This data is available through various wearable devices, mobile apps, social media, web engine searches, environmental sensors etc. Much of this data is produced continuously and can be analyzed in real-time using application platform interfaces and can be linked with public health data like census and health surveys, through AI.

Few areas where India has been working with AI technologies in the public health ecosystem.

  • Cancer
  • AI has tremendous scope in cancer screening and treatment and early detection can help in better prognosis of the disease. An AI-based breast cancer screening device that uses a non-invasive, low-cost solution based on heat mapping for early detection of breast cancer has been able to detect breast cancer up to five years earlier than mammography with reduced reliance on trained technicians. It has been taken up as a public-private partnership pilot project in some states.
  • NITI Aayog is in the advanced stage of launching a program to develop a national repository of annotated and curated pathology images. The components of such a repository include a move towards “Digital Pathology”, which entails all glass slides generated being scanned at high resolution and magnification, followed by accurate, precise, and comprehensive annotation of the scanned images using various data sources and levels of clinical and pathological information available from day-to-day patient care.
  • Imaging Biobank for cancer – a proposed project of NITI Ayog – where AI-based Radiomics will be utilized for improving the scope of existing biomarkers. AI-based Radiomics is an emerging field that refers to the comprehensive quantification of tumour phenotypes by applying many quantitative imaging features.
  • Diabetic retinopathy is among the most common complications of diabetes. NITI Aayog is working with Microsoft and Forus Health to roll out a technology for the early detection of diabetic retinopathy as a pilot project. 3Nethra, developed by Forus Health, is a portable device that can screen for a common eye problem. Integrating AI capabilities to this device using Microsoft’s retinal imaging APIs enables operators of the 3Nethra device to get AI-powered insights even when they are working at eye checkup camps in remote areas with nil or intermittent connectivity to the cloud.
  • Maternal and child health –
  1. Khushi AI model: Public data collected either manually or digitally has variations because the way data is collected and interpreted is not the same any two individuals. Khushi Baby collaborated with researchers from Google AI for social good and created a machine learning model to classify health workers by their data quality phenotype. This algorithm helped them understand the gaps and the amount of training supervision required for health workers for data collection. Khushi Baby mobile application- covers an integrated digital health census, family planning, antenatal care, labour monitoring, immunization, and child health. The Khushi AI is also being used to spotlight vulnerable communities and to optimize communication interventions to improve health outcomes.
  • mMitra program – ARMMAN- started in 2013 – a free service sending voice calls with critical preventive care information directly to the mobile phones of women, covering the period from pregnancy till the child turns one. Currently, Google and IIT Madras are working on the design of AI technology that can predict the indication of women who would drop out from the mMitra program. mMitra has reached over 2.3 million women in India and is 1 of only 5 scaled mobile-based maternal messaging programs in the world.

Potential areas where AI can be adopted and utilized in the field of Maternal and child health –

  1. Predictive modelling for high-risk factors during pregnancy – AI algorithms can help in identifying risk factors and thereby identifying high-risk pregnancies
  2. Predicting birth weights will help lower the infant mortality rate and address malnutrition issues.
  3. AI can be used to detect fetal abnormalities.
  4. AI can be used to improve access to care – by sharing timely relevant personalized messages with pregnant mothers.
  • AI Healthcare startups

Many companies have launched healthcare services using AI to capture and analyse health-related data.

  1. Qure.ai – utilizes deep learning technology to provide automated interpretation of radiology exams like X-rays, CT scans, and ultrasounds, enabling faster diagnosis and treatment of malignant diseases.
    1. Healthify Me- app utilizes a virtual assistant “Ria”, the world’s first AI nutritionist that addresses queries around fitness, nutrition and health in ten different languages.
    1. Dozee- provides contactless health monitors that silently track heart, respiration, sleep patterns, stress levels, cardiac contractions, apnea, and more while sleeping. Dozee’s integration with pulse oximeters was used in providing continuous remote monitoring to Covid-19 patients and keeping doctors safe from exposure to infection during the pandemic.
    1. Perfint Healthcare uses AI algorithms for image-guided interventional procedures, especially in oncology and pain care. The company’s products include Robio EX, Maxio, and Navios. Robio EX is a CT & PET-CT guided robotic positioning system, whereas Maxio provides intraoperative guidance and post-procedure verification support. Navios is a computer-based workflow assistance solution for CT-guided percutaneous ablation procedures.
  • Surveillance – Public health surveillance is usually performed using population health surveys and clinical data but access to new data sources and their amalgamation with AI technology can help identify emerging health threats and develop a more detailed understanding of population disease and risk factor distributions, often with improved geographic resolution.
  • ABDM (Ayushman Bharat Digital Mission)

The vision towards launching a nationwide digital mission was to create a platform that allowed interoperability of health data within the healthcare ecosystem so that all the data are interlinked and stored under one Digi locker as an Electronic medical record and accessible to the user through a unique ID – ABHA ID when in need of health emergencies. One use case of AI algorithms that have been incorporated in ABDM is the digitization of the prescription (voice-based – e-prescription). This will help achieve standardization, and reduce human error. Around fifty-two digital applications have been integrated into ABDM through various APIs and AI algorithms within these applications capture data in a structured standardized format. Through the ABHA number, the user can easily scan and get an appointment and their medical records are also stored.

  • AI and PMJAY scheme

PMJAY is facing many strategic challenges like data management of patients, utilization of services, enrolment data of citizens, the cost of services, and the quality of care. These areas can be to some extent addressed with AI technology. In the case of minimizing the cost of services and improving the quality of care – AI technology can be adopted to enable tests to be performed in a shorter time span and without a technician. This will help in standardization (reducing human error) and ensure timely and quality care. Another application can be on the supply chain side where AI can automatically send inventory orders when the level of medicines reaches a certain threshold. AI algorithms can help detect fraud by keeping a check on irregularities caused due to over-billing or testing or wrong beneficiary information. The data security and the accessibility of health records by the patient can be managed by deep learning algorithm of the AI technology. ABDM has addressed this issue through various platform integration and the generation of a unique ID – ABHA number.

  • AI applications for resilient health systems

The best example of AI applications for resilient health systems was during the COVID-19 pandemic. Artificial intelligence and machine learning opened new channels for effective healthcare during this pandemic. AI and ML can be useful for medicine development, designing efficient diagnosis strategies and producing predictions of disease spread. Some of the examples of AI technology applied during Covid 19 pandemic are Outbreak Response Management and Analysis System (SORMAS) and HealthMap, two surveillance-mapping tools that work online and allow early detection of contagious diseases in contrast to commonly used epidemiological methods. The deep learning AI process applied to CT scan images helped identify and validate the stages more accurately. AI and ML can help to improve monitoring and identify abnormal patterns or behaviours of patients remotely by doctors thereby preventing the exposure of medical professionals to high-risk patient populations.

Research in India  

India has all the blocks needed for the adoption and implementation of AI systems including the research and development ecosystem, AI experts and professionals, and top-notch IT companies. And yet, India ranks a dismal 19th globally when we look at the county-wise H-index (a metric that quantifies a country’s scientific productivity and scientific impact). The reasons for this are a) lack of collaboration- research is being done in silos in academic institutions b) lack of facilities to support large-scale experiments c) lack of connections with stakeholders and practitioners to convert the output into outcome.

To address some of the above issues and improve the research for AI technology, the Inter-Ministerial National Mission in their detailed project report on Interdisciplinary Cyber-Physical Systems (IM-ICPS) has suggested the following four-tier framework for promoting research focused on all aspects of the technology life cycle: research, technology deployment, translation, and management:

  • ICON (International Centers of New Knowledge): focuses on the creation of new knowledge through basic research,
  • CROSS (Centre for Research on Sub-Systems) focuses on developing and integrating core technologies developed at the ICON layer and other sources.
  • CASTLE (Center for Advanced Studies, Translational Research and Leadership): focusing on the development and deployment of application-based research and
  • CETIT (Centre of Excellence in Technology Innovation and Transfer): focusing on the commercialization of technologies developed.

To make the above recommendations seamless for the adoption a two-tier integrated approach to boost both core and applied research in AI is proposed-

  • COREs (Centres of Research Excellence in Artificial Intelligence): COREs will focus on core research of AI and will take on the mantle of executing the responsibilities of both ICON and CROSS as per the IM-ICPS framework.
  • ICTAI (International Centre for Transformational Artificial Intelligence):-responsible for delivering technology and converting prototypes into marketable products


Healthcare is embracing AI-based digital health innovation to respond to critical health challenges. But the risks associated with the use of AI are not fully understood. In India, the government owns large data sets both from public health facilities and from national programs. However, this data lacks accuracy and completeness leading to incomplete conclusions. AI is based on the foundation of robust and accurate data. The collection of this primary data is cumbersome and requires intensive capital investments to convert it into tools for the public health system. Therefore, a critical challenge for the government is to enable the digitization of most of the data sets available in the public health ecosystem. Thereafter, it is necessary to develop a data culture and quality systems to enable digital health data to accurately depict the realities of health outcomes at the population, sub-national and even individual facility, or patient level.

Extensive evaluation and testing processes of tech solutions result in prolonged time spent at this stage, delaying the time for the deployment, and requiring a relatively longer period of lock-in for investors. There is also increased ambiguity around the perceived risk of AI-based technology, and it would require stricter vigilance that follows post-market modification.


While AI has immense potential in meeting the needs of the under-resourced and overburdened public health system, a lot needs to be done to regulate and structure its usage and optimize its benefits. Investments would be required to build the digital health ecosystem in the country to unlock the large amount of data that exists with the stakeholders, which forms the base of AI-driven system deployment.

References –

1) Deloitte survey report (2018)


2) Niti Ayog’s – National Strategy for Artificial Intelligence (2018)


3) Niti Ayog’s – Responsible AI (2021)


4) WHO guidance on Ethics & Governance of Artificial Intelligence for Health (2021)


5) https://bangaloreai.com/blog/

  1. https://www.khushibaby.org/

Photo Credits: Forbes

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