India joins global AI health regulation efforts, emphasizing safety, ethics, and inclusivity in AI-driven diagnostics, treatment, and data management to improve healthcare outcomes through innovation and collaboration.

Why in the News?

India joins Health AI Global Regulatory Network (GRN) to strengthen oversight of AI in healthcare.

More on the News:

HealthAI is a Geneva-based, independent non-profit organization that aims to advance development and adoption of Responsible AI solutions in health through collaborative implementation of regulatory mechanisms and global standards.
GRN members have exclusive access to the 'Global Public Repository of AI-related Registered Solutions for Health', where participating regulatory authorities can showcase AI-related registered solutions from their countries.
- Indian Council of Medical Research -National Institute for Research in Digital Health and Data Science (ICMR - NIRDHDS) & IndiaAI will work with HealthAI alongside fellow GRN members, such as UK and Singapore.
It supports IndiaAI strategy, which aims to build a comprehensive and inclusive AI ecosystem.
- IndiaAI operates under Ministry of Electronics & Information Technology (MeitY) via the Digital India Corporation. It aims to position India as a leader in AI innovation and development.

Need for strengthening oversight of AI in Healthcare:

Patient Safety and Risk Minimization: AI in healthcare directly impacts diagnosis and treatment, so regulation is required to ensure safety, minimize risks, and prevent harm.
Data Privacy and Security: Healthcare AI uses sensitive patient data, making regulation vital to protect privacy and ensure data security. India's ICMR ethical guidelines emphasize safeguarding personal health data at various stages.
Ethical Use and Fairness: AI can exhibit biases based on training data, risking unfair discrimination. Regulations are needed to ensure fairness, non-discrimination, and ethical deployment of AI tools.
Transparency and Accountability: Many AI systems operate as "black boxes" i.e. they generate results without explaining how they arrived at them. Regulators are likely to insist manufacturers explain how these devices arrive at decisions.
Managing Liability: The potential utilization of AI in healthcare has raised substantial concerns regarding the assignment of liability for medical errors arising from AI-augmented healthcare delivery.

Applications of AI in Healthcare:

AI in Diagnostics	AI in Hospitals and Clinical Settings	AI in Health Data Management
Early Detection: iOncology.ai (AI platform developed by AIIMS and CDAC) helps in early detection of cancer. Drug Discovery and Development: accelerates creation of new medications by analysing molecular structures and identifying candidates for clinical trials	Robotic surgeries- da Vinci robot (AIIMS, Delhi) performs complex procedures with precision. Virtual Health Assistants- 'Clinical Decision Support System' integrated into eSanjeevani provides AI-based differential diagnosis recommendations.	Electronic Health Records- helps find patterns in patient's data and make better decisions. Eg- AyushMaan Bharat Digital Health Mission. Automating mundane tasks: such as data entry, claims processing and appointment scheduling etc.

Challenges of integrating AI in Healthcare:

Technological Challenges

Interoperability and Standardization: Diverse healthcare technologies and limited standardization hinder AI integration and raise risks of unauthorized data access.
Algorithmic Bias: E.g. predictive models inaccurately assessed Black patients' health needs which used cost as a proxy for healthcare needs.

Ethical Challenges

Justice and Fairness: AI should enable equitable access and unbiased decisions, preventing the widening of healthcare inequalities.
Patient Consent and Privacy: AI must safeguard sensitive health data with strong consent and protection mechanisms.
Misinformation and Dehumanization: AI errors or overreliance can spread misinformation and reduce meaningful patient-provider interactions.

Inclusivity and Access Challenges

Representation bias: Representation bias is present when samples from urban, wealthy, or connected groups lead to the ignoring of samples from rural, indigenous, or disenfranchised groups
Resistance and Trust: Healthcare professionals may resist AI adoption due to limited understanding, fear of job loss, or doubts about AI reliability.

Global Innovations India Can Learn Through

AI for Remote Patient Monitoring: Finland uses AI-enabled wearables to monitor chronic patients in real-time, reducing hospital visits and offering a model for India's non-communicable diseases management.
Federated Learning for Data Security: Singapore applies federated learning to train models on decentralised data without sharing sensitive information, a practice India can adopt under Ayushman Bharat for stronger privacy safeguards.

Recommendations for integrating AI in Healthcare in India

WHO's guiding principles for use of AI in Healthcare

Protect Autonomy
Promote Human Well-being, Human Safety, and the Public Interest
Ensure Transparency, Explainability, and Intelligibility
Foster Responsibility and Accountability
Ensure Inclusiveness and Equity
Promote AI That Is Responsive and Sustainable

Improving data diversity and reducing bias: Training AI on diverse socio-economic, geographic, and demographic datasets enhances accuracy and reduces bias.
Bridging the Urban-Rural Divide: E.g.: AI-powered telemedicine connects urban doctors with rural communities, expanding equitable healthcare access.
Embrace regulatory sandboxes approach: Countries like the USA, Canada, Japan, and Indonesia use sandboxes to foster regulatory innovation and evaluate AI-driven digital health solutions.
Use 'Human in The Loop' model of AI technology which gives room for humans to oversight the functioning and performance of the system.

Conclusion:

AI offers transformative potential for diagnostics, treatment, and healthcare access, but must be regulated to address challenges of safety, bias, privacy, and inclusivity. Coordinated efforts in innovation, capacity building, ethical governance, and international collaboration will enable India to harness AI for improved healthcare outcomes at scale.