AI in Indian Healthcare: Challenges, Policies, and Way Forward

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Context

AI is transforming healthcare through diagnostics, predictions, streamlined practices, improved management, drug discovery, and research, but faces low adoption despite the development of new tools.

AI in Indian Healthcare 

Healthcare challenges in accessibility, affordability, and quality, especially in rural areas due to professional shortages and poor infrastructure, are being addressed by Artificial Intelligence (AI) through enhanced diagnostics, personalized treatments, and workflow optimization.

The Indian health technology market is estimated to grow at an annual rate of 22%, reaching US$ 35.8 billion by 2030, with the digital health market expanding from US$ 12.20 billion in 2023 to US$ 25.64 billion by 2027. (Source: IBEF)

The healthcare workforce is projected to grow from 7.5 million to 9 million by 2027, with 1-2% comprising technology experts, creating 2.7-3.5 million new technology jobs. (Source: IBEF)

What are the Applications of AI in Healthcare?

Disease Diagnosis and Screening

AI algorithms improve the speed and accuracy of diagnostics by analyzing medical images like X-rays, CT scans, and MRIs to detect anomalies that may be missed by the human eye.

• Case Study: Niramai's Thermalytix: This Bengaluru-based startup uses AI and thermal imaging for radiation-free, non-invasive breast cancer screening. 
• Thermalytix analyzes thermal patterns to detect early-stage abnormalities, improving screening accessibility, especially in rural India.
• Case Study: Netra.ai for Diabetic Retinopathy: A Google and Sankara Eye Foundation collaboration, Netra.ai uses deep learning on retinal images to detect diabetic retinopathy (DR). 

Public Health Surveillance

The Integrated Disease Surveillance Programme (IDSP) under the National Centre for Disease Control (NCDC) utilizes an AI-powered tool to detect early warning signals of potential disease outbreaks by scanning millions of online news reports and social media feeds daily.

Clinical Decision Support & Hospital Management

AI-powered systems assist clinicians in making faster, more informed decisions and help optimize hospital operations for better efficiency.

• Apollo Hospitals utilizes an AI tool, trained on Indian data, to predict cardiovascular risk more accurately than standard global models.
• National Telemedicine Service (eSanjeevani): It incorporates AI-based Clinical Decision Support Systems (CDSS) to assist doctors, enhancing the quality and consistency of remote medical advice for millions.  

Government's Policy Framework for AI in Healthcare

What are the Challenges Hindering AI Adoption?

Data-Related Challenges

The effectiveness of AI depends on large, high-quality datasets. Key issues include a lack of standardized data, poor data quality, interoperability problems, and bias in datasets that often do not represent India's diverse population.

Infrastructure and Skill Gaps

The digital divide, characterized by limited rural connectivity and digital infrastructure, is a major barrier, compounded by a critical shortage of professionals with dual expertise in AI and medicine, as reported by NASSCOM.

Ethical and Regulatory Concerns

A lack of a specific legal framework for AI creates ambiguity regarding liability and accountability for AI-generated errors. Ensuring patient consent and data privacy, in line with the Supreme Court's Puttaswamy judgement (Right to Privacy), is paramount.

High Cost and Lack of Trust

The high initial cost of developing and implementing AI solutions is prohibitive for many smaller healthcare facilities. Building trust among clinicians and patients requires transparent and "explainable AI" (XAI) models whose decisions can be understood by humans.

Way Forward

To harness AI's full potential, India needs a multi-pronged, collaborative, and inclusive strategy guided by a 'SAFE-AI' framework:

• S - Skilling and Capacity Building: Invest in integrating data science and AI into medical curricula and upskilling the existing healthcare workforce to effectively use AI tools.
• A - Accessible and Actionable Data: Strengthen ABDM for high-quality, interoperable health data. Encourage diverse datasets to reduce algorithmic bias, ensuring AI benefits all.
• F - Fair and Forward-looking Regulation: Establish a clear legal and regulatory framework for AI in healthcare, defining accountability, protecting data privacy, and ensuring transparent validation and post-market surveillance of AI tools.
• E - Ecosystem for Innovation: Encourage public-private partnerships (PPPs) among academia, startups, and hospitals to develop AI solutions specific to India's unique healthcare challenges.

Learn Lessons from Global Best Practices

UK's NHS AI Lab

Provides a model for a state-led initiative that helps test, evaluate, and scale safe and ethical AI technologies within a public health system, creating sandboxes to validate AI tools before large-scale deployment.

US FDA's Regulatory Framework

The U.S. Food and Drug Administration's risk-based framework for "Software as a Medical Device" (SaMD) offers a clear pathway for validating and certifying AI/ML tools, ensuring their safety and reliability.

WHO's Guidance on AI Ethics

The World Health Organization (WHO) outlines six core principles for ethical AI in health: protecting human autonomy, ensuring safety, ensuring transparency, fostering accountability, ensuring equity, and promoting sustainable AI. 

Conclusion

Artificial Intelligence promises to revolutionize Indian healthcare, and with a focus on policy, ethics, and skill development, India can leverage this potential to achieve universal and equitable health coverage.

Source: PIB