If you are interested in applying to GGI's Impact Fellowship program, you can access our application link here.
Artificial Intelligence (AI), otherwise known as the new electricity, has gathered the recent focus in the world. AI enables the machine to perform cognitive processes like learning, decision making, thinking, perceiving, problem-solving coupled with big data, collection, aggregation and analysis. It uses historical data to learn and use it to extrapolate and predict the future outcomes with an unmatchable pace when compared to human brain.
Fig 1: What is Artificial Intelligence (Source: NITI Aayog, Government of India. 2018. “National Strategy for Artificial Intelligence)
AI can be the next general-purpose technology, similar to electricity and the internet. This is because of its wide scope of industrial application which generates economic value with increased productivity and better decision-making.
2. AI In Healthcare
“Artificial Intelligence (AI) has the potential to transform healthcare in various ways. It can turn large amounts of patient data into actionable information, improve public health surveillance, accelerate health responses, and produce leaner, faster and more targeted research and development” (Raghupathi and Raghupathi 2014) 1.
There are 3 broad categories of using AI in healthcare (Paul et al. 2018 2; Raghupathi and Raghupathi 2014):
a) Descriptive: the future trends and insights are based on the quantification of previous events.
b) Predictive: It involves making future predictions based on descriptive data
c) Prescriptive: This not only identifies trends and forecasts the future, but also offers potential public health treatment solutions or clinical trials for research and development.
Potential use cases of AI in Healthcare
Fig 2: Potential use cases of AI in Healthcare (Source: NITI Aayog, Government of India. 2018. “National Strategy for Artificial Intelligence)
AI has a wide range of potential applications in the field of health. These applications include data mining from medical records, creating treatment plans, predicting the occurrence of health events like dengue or malaria, helping with repetitive tasks, conducting online consultations, and assisting with clinical decision-making by, for example, analyzing radiology images to spot and detect problems, managing medications, creating new drugs, making healthier decisions, and solving public health issues by analyzing enormous amounts of real-time data to identify problems (Gupta and Kumari 2018). The fact that artificial intelligence in healthcare could be very beneficial in locations with a shortage of human resources, particularly in rural and distant areas, is one of its largest advantages.
AI can assist in the development of self-learning digital systems in areas like radiology, genomics, and pathology to advance the future of healthcare includes: creating electronic health data repositories with sufficient high-quality annotated health data for machine learning applications; developing a national clinical decision support system that could improve the management of routine clinical problems by less-skilled healthcare providers. (Malhotra and Roy 2019).
Notwithstanding the advantages of this new technology that have already been described, its application still poses a serious problem that has to be solved.
3. AI in Indian Healthcare System
Artificial Intelligence (AI) has immense potential to transform the Indian healthcare system by improving patient outcomes, increasing efficiency, and reducing costs. However, its adoption in the country's healthcare system is still in its nascent stages.
The Indian government has recognized the importance of AI in healthcare and has taken steps to promote its use. In 2018, the government launched the National Health Stack, a digital infrastructure that aims to provide a standardized and interoperable framework for healthcare delivery in the country. The government has also set up the National Digital Health Mission, which aims to create a national digital health ecosystem to support the delivery of healthcare services.
In addition, the government has launched various initiatives to promote the use of AI in healthcare. For example, the Ministry of Health and Family Welfare has launched the National Health Stack Sandbox, which provides a platform for developers to create and test healthcare applications using AI and other emerging technologies.
Despite these efforts, the adoption of AI in healthcare in India faces several challenges. These include a lack of standardization in healthcare data, inadequate infrastructure and funding, and a shortage of skilled personnel.
Overall, the Indian government's role in promoting the use of AI in healthcare has been positive, but more needs to be done to overcome the challenges and realize the full potential of this technology in improving the country's healthcare system.
4. Implementation Challenges
’According to PwC India’s 2020 survey of C-level executives and decision makers in the healthcare and pharmaceutical industries, compliance and control risks were identified as major challenges when it comes to adopting AI. Specifically, 53% of respondents identified compliance risk – which refers to the potential for AI systems to be in violation of laws and regulations related to patient privacy, data security and clinical decision making – as a significant challenge. Control risk was identified as a significant challenge by 50% of respondents. It involves the detection of rogue AI systems and unintended consequences, as well as lack of human oversight in the decision-making process.’’
4.1 Access to high quality data
The implementation of AI in healthcare in India faces significant challenges due to a lack of quality data, fragmentation of medical data and absence of standardized medical data. These challenges are interconnected and must be addressed together to effectively integrate AI into healthcare.
Healthcare providers need top-notch datasets to validate AI models both technically and clinically. However, gathering patient data and images for testing AI algorithms is challenging due to the fragmentation of medical data across various EHRs and software platforms. In addition, medical data from one organization may not be compatible with other platforms, leading to interoperability issues. To overcome these hurdles and increase the volume of data available for testing AI systems, the healthcare sector must focus on standardizing medical data.
4.2 Cultural challenges
The cultural challenges associated with the adoption of AI in healthcare in India are multifaceted and complex. India is a culturally diverse country, with a rich heritage of traditional healthcare systems that are deeply rooted in cultural practices and beliefs. The introduction of AI in healthcare has the potential to disrupt these traditional systems and practices, leading to a sense of cultural displacement and the erosion of traditional healthcare systems.
Another cultural challenge is the perception of AI as a dehumanizing force in healthcare. There may be patients wanting the presence of a physical doctor or an elderly who may be more averse to adopting such technology.
4.3 Interoperability and infrastructure concerns
Interoperability refers to the ability of different healthcare systems to exchange information and communicate with each other seamlessly. In India, healthcare data is often stored across different systems, making it difficult for healthcare providers to access patient information when they need it. This lack of interoperability can be a significant barrier to the adoption of AI in healthcare, as AI systems require large volumes of data to be effective. In the European Union, countries like Spain and Ireland have more than 90% of their data available as open data, whereas such statistics are not available for India.
Another significant challenge is the lack of infrastructure to support the adoption of AI in healthcare in India. Many healthcare facilities in India lack the necessary technology infrastructure, such as high-speed internet and data storage capabilities, to support the adoption of AI. Without the necessary infrastructure, healthcare providers may be unable to leverage the benefits of AI, such as faster and more accurate diagnoses and treatment plans.
The lack of infrastructure is particularly pronounced in rural areas, where healthcare facilities often lack access to basic amenities such as electricity and running water, let alone the latest technology. This disparity between rural and urban healthcare facilities could exacerbate existing health inequalities in India and limit the potential impact of AI in improving healthcare outcomes.
4.4 Data security and privacy
As healthcare data is highly sensitive and personal, it must be handled with utmost care to ensure patient privacy and data security.
One of the primary concerns related to data safety and privacy is the potential for data breaches. Hackers can exploit AI solutions to collect private and sensitive information such as Electronic Health Records. Machine learning algorithms can also be misused to develop autonomous techniques that jeopardize the security and safety of such vital information. Such breaches can have serious consequences, including identity theft, financial fraud, and reputational damage to healthcare organizations.
Another challenge related to data privacy is the issue of informed consent. Informed consent is essential for patients to understand the purposes and potential risks associated with the collection and use of their healthcare data. In India, many patients may not be aware of their rights and the implications of sharing their healthcare data with healthcare providers.
The implementation of AI in healthcare raises questions about accountability. While AI is intended to provide decision support, with a human in the loop to interpret and correct errors, there are concerns about the capacities and incentives of these individuals to challenge AI-generated results. In rural areas, front-line health workers may lack the necessary knowledge and training to interpret and correct AI-generated results, while overwhelmed doctors may not have the time or incentive structures to do so. The weak regulation of the Indian health sector further complicates the issue of accountability, as evidenced by numerous reports of negligence and malpractice in even well-established private hospitals.
The use of individual health data in decision-making processes such as credits or loan applications also raises concerns about accountability. Predictive and self-learning algorithms must be audited not only for efficiency and accuracy, but also for biases and knock-on effects. Fairness, accountability, and transparency frameworks are being explored, but the implications of the use of AI by different practitioners, such as doctors versus community health workers, must also be considered.
The use of AI is likely to transform patient-doctor relationships and related systems of trust. Safeguards must be put in place to build trust and encourage patient buy-in, including processes of deliberation and explanation.
4.6 Policy and regulation
Policies and regulations play a crucial role in shaping the adoption of AI in healthcare in India. A clear and well-defined policy framework provides a structured approach to the development, implementation, and monitoring of AI-based solutions in healthcare. It helps ensure that AI applications are developed and used ethically and with a focus on patient safety and privacy.
The National Strategy for Artificial Intelligence (NSAI) and the approach paper under the "Towards responsible AI for all" strategy released by NITI Aayog provide a policy framework for the development and deployment of AI in healthcare in India. These policies aim to ensure that AI applications are developed responsibly and used for the betterment of society. They also provide guidelines for data privacy, security, and accountability in AI-based healthcare solutions.
The policies and regulations have also helped to build trust in AI-based healthcare solutions among patients and healthcare providers. Patients are more likely to use AI-based healthcare solutions when they are assured of the safety and privacy of their personal data. Healthcare providers are more likely to adopt AI-based solutions when they are confident in the reliability and accuracy of the solutions.
Furthermore, policies and regulations can also encourage investment in the development and implementation of AI-based healthcare solutions in India. A clear policy framework provides a level of certainty and predictability for businesses looking to invest in AI-based healthcare solutions. This can help spur innovation and drive growth in the healthcare sector.
In conclusion, policies and regulations play a crucial role in shaping the adoption of AI in healthcare in India. They provide a structured approach to the development, implementation, and monitoring of AI-based solutions, build trust among patients and healthcare providers, and encourage investment in the sector.
In recent years, AI has made significant strides in the healthcare industry, providing innovative solutions for complex medical challenges. In this research paper, we have explored the potential use cases of AI in healthcare, its current implementation in the Indian healthcare system, and the challenges it faces. In this final section, we will provide recommendations to overcome these challenges and enhance the adoption of AI in healthcare in India.
5.1 Behavioural Change: Nudging
One of the biggest challenges in implementing AI in healthcare is getting people to adopt the technology. Behavioural change interventions such as nudging can be used to encourage patients, healthcare providers, and other stakeholders to embrace AI solutions. Nudging involves using subtle prompts or incentives to influence behaviour. In the context of AI in healthcare, nudging could involve providing patients with personalized health recommendations based on their data, encouraging healthcare providers to use AI-based tools, and incentivizing hospitals to invest in AI infrastructure.
5.2 Training at various platforms
Another important recommendation is to provide training on AI at various levels in the healthcare system. This could include training for healthcare providers, policymakers, and patients. Healthcare providers need to be trained on how to use AI-based tools and interpret the results. Policymakers need to be educated on the potential benefits and challenges of AI in healthcare to develop appropriate policies and regulations. Patients need to be informed about the benefits of AI and how it can improve their health outcomes.
5.3 Capacity Building
Capacity building is another critical recommendation for the successful adoption of AI in healthcare. Capacity building involves developing the necessary infrastructure and resources to support the adoption of AI-based solutions. This could include investing in digital infrastructure, such as high-speed internet, cloud computing, and data storage facilities. It could also involve developing the necessary human resources, such as data scientists, machine learning engineers, and AI experts.
5.4 Trust Building
Trust is a critical factor in the successful adoption of AI in healthcare. Patients and healthcare providers need to trust AI-based solutions to use them effectively. Trust can be built by ensuring that AI-based solutions are transparent, secure, and reliable. This could involve providing patients with clear information about how their data will be used and ensuring that their privacy is protected. It could also involve developing transparent and accountable AI algorithms that can be audited and reviewed by healthcare providers and patients.
Finally, obtaining informed consent from patients is another important recommendation for the successful adoption of AI in healthcare. Patients need to be informed about the benefits and risks of using AI-based solutions and should have the right to choose whether to use them or not. This could involve developing clear and concise consent forms that explain how AI will be used, the risks and benefits, and how patient privacy will be protected.
AI has the potential to transform the healthcare industry in India. However, to realize its full potential, there are several challenges that need to be addressed. The recommendations outlined in this research paper, including behavioural change, training, capacity building, trust building, and consent, can help overcome these challenges and enhance the adoption of AI in healthcare in India. By implementing these recommendations, we can improve healthcare outcomes, reduce costs, and provide better access to healthcare for all.
Meet The Thought Leader
Karan Patel (he/him) is a mentor at GGI an undergraduate from IIT Madras. He is correctly employed with Teachmint, an ed-tech start-up in their strategy team. Prior to Teachmint, he worked at Dalberg Advisors as an analyst where he worked with multi-laterals and international foundations on gender, education and energy sectors. He has also interned in MIT Sloan, Qualcomm and IIM Ahmedabad giving him a plethora of experience in the corporate and academic world. He also started his own venture in hyperlocal air-quality monitoring. Karan is an avid sport-person and masala chai fanatic.
Meet The Authors (GGI Fellows)
Kavyashree Satish is a driven professional with a background in biotechnology and research. She holds a Master's degree from Pennsylvania State University and worked as a Research Scientist in vaccine research for the Naval Medical Research Center for six years. Currently on an experimental sabbatical, Kavyashree is focusing on her health and travel, while also pursuing her interest in healthcare consulting.
During her sabbatical, Kavyashree has also started an organic skincare online store and volunteers as a project lead for Dheerya Foundation, an NGO that focuses on equitable education for children from underserved communities. She has also co-authored scientific research papers during her time as a scientist.
Outside of work, Kavyashree is an avid traveler and enjoys exploring new cultures and cuisines. In her free time, Kavyashree can be found curled up with a good science fiction book. With her impressive background and diverse range of experiences, Kavyashree is poised to make a significant contribution to the healthcare consulting industry.
Dr. Vaibhav Sindhi is a dynamic and passionate individual with a diverse background in social work, healthcare and entrepreneurship.
His experience begins with as a founder of eStudy Partner, a consulting firm focused on overseas dental education. Later during the second deadly COVID-19 wave he served the nation as a frontline COVID-19 warrior with the Ministry of Health & Family Welfare. Currently he works as Program Manager with Lifeline Foundation an NGO focused on educating the nation from Road Safety to Primary Trauma Care.With a broad range of skills and experiences, Vaibhav is a well-rounded professional committed to making a positive impact in the world.
Parth Shukla is a recent graduate of UBC Sauder School of Business, he has gained valuable private equity experience, which has honed his problem-solving and leadership skills.
Most recently, he is working in Deloitte's business tax team, where he is learning the intricacies of tax law and developing strong attention to detail. His experience as an Impact Fellow at the Global Governance Initiative (GGI) has further pushed him to pursue a career in problem-solving and consulting, by providing training in critical reasoning and frameworks used in consulting. GGI's ecosystem, designed for those with "ambition," has inspired him to make a positive impact on businesses by finding solutions to complex issues. He is committed to developing his skills and helping his clients thrive in the consulting industry.
Neha Singh is a Master’s degree holder from BIT, Mesra. She is currently an aspiring UPSC aspirant. Before that, she worked as a scientist in RnD at Biocon, one of Asia’s largest Biopharmaceutical companies. She is also passionate about impacting society and hence her quest for UPSC.
In her free time, she likes to cook and travel. She also holds a diploma in singing and loves to sing Hindi classics.
If you are interested in applying to GGI's Impact Fellowship program, you can access our application link here.
1. Raghupathi, Wullianallu, and Viju Raghupathi. 2014. “Big Data Analytics in Healthcare: Promise and Potential.” Health Information Science and Systems 2 (3). https://www.researchgate.net/publication/272830136_Big_data_analytics_in_healthcare_Promise_and_potential.
2. Paul, Yesha, Elonnai Hickok, Amber Sinha, Udbhav Tiwari, and Pranav M Bidare. 2018. “Artificial Intelligence in the Healthcare Industry in India,” 45. https://cisindia.org/internet-governance/files/ai-and-healtchare-report.
3. NITI Aayog, Government of India. 2018. “National Strategy for Artificial Intelligence #AIFORALL.”https://niti.gov.in/writereaddata/files/document_publication/NationalStrategy-for-AIDiscussion-Paper.pdf.
4. Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. Penguin. 5. Wartman, S. A., & Combs, C. D. (2018). Medical education must move from the information age to the age of artificial intelligence. Academic Medicine, 93(8), 1107-1109. 6. Sharma, A., & Agarwal, R. (2020). Artificial intelligence and its potential in healthcare in India. Journal of Medical Systems, 44(8), 143. 7. Verghese, A. (2018). Culture shock—Patient as icon, icon as patient. New England Journal of Medicine, 379(7), 595-597. 8. Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016). Machine bias. ProPublica. 9. Rigby, M. J., & Rigby, P. C. (2018). Improving informed consent: Nudging and social influence. Journal of Law and the Biosciences, 5(1), 1-26.a