AI in Health: India Joins Health AI Global Regulatory Network (GRN)

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Context

India joined the HealthAI Global Regulatory Network (GRN), a platform to strengthen AI oversight in healthcare, collaborating with countries like the UK and Singapore.

HealthAI Global Regulatory Network (GRN)

It is an international platform that brings together national health regulators to strengthen oversight of artificial intelligence (AI) in healthcare

It is an initiative of HealthAI—The Global Agency for Responsible AI in Health, a Geneva-based, independent non-profit organization. 

The GRN facilitates collaboration to ensure that AI-driven solutions for health are safe, effective, and ethical. 

Artificial intelligence (AI) in Healthcare

AI in healthcare uses machine learning and natural language processing to improve diagnostics, drug discovery, personalized treatments, and administrative tasks by analyzing vast amounts of patient and medical data

Improved Diagnostics: AI enhances diagnostic accuracy by analyzing medical images like CT scans and MRIs. For example, Bengaluru-based Sigtuple’s AI platform remotely analyzes blood samples, enabling specialist diagnostics in underserved areas.

Personalized Treatments: AI customize treatment plans using patient data. Apollo Hospitals’ AI-Precision Oncology Center customizes cancer treatments, improving outcomes.  

Efficient Workflows: AI automates tasks like scheduling and clinical documentation.  

Telemedicine Expansion: AI-powered telemedicine platforms like Practo remove language barriers, making healthcare accessible in rural areas.

Economic Impact: The Indian AI healthcare market is projected to reach USD 1.6 billion by 2025, with a 40.6% CAGR, contributing $ 25–30 billion to GDP. (NASSCOM Report).

India’s Role in HealthAI Global Regulatory Network (GRN)

Collaboration: India, through ICMR-NIRDHDS and IndiaAI, shares safety protocols and monitors AI performance with GRN members, fostering global standards.

Global Leadership: India’s healthcare experience strengthens GRN’s goal of equitable AI-driven health solutions, enhancing its influence in global AI governance.

Access to Resources: GRN membership provides India access to a global directory of AI health tools, boosting transparency and innovation.

Challenges in Implementing AI in Healthcare

Infrastructure Gaps: Many healthcare facilities, especially in rural areas, lack the basic technological infrastructure needed to support complex AI systems, such as consistent electricity, high-performance computing, and data storage capabilities.

Data Issues: Fragmented healthcare data and lack of standardization hinder AI model training. India lacks integrated electronic health record (EHR) systems.  

Digital Divide: 45% of Indians lack internet access, restricting AI benefits to urban areas. Tele-density in rural areas is 59.43%.

Regulatory Delays: The Digital Information Security in Healthcare Act (DISHA), proposed in 2017, remains unenacted, creating uncertainty for AI developers.

Skill gaps: Shortage of professionals with combined expertise in AI, data science, and clinical practice.

Accountability: It remains unclear who is responsible when an AI system makes a mistake, especially with "black box" algorithms where the decision-making process is opaque.

Language barriers: India's linguistic diversity makes it challenging to create AI systems that can effectively communicate with patients across different regions.

Ethical Concerns: Algorithmic bias from non-representative datasets risks inaccurate diagnoses. For example, AI models trained on Western data may not suit India’s diverse population.

High Costs: Initial investment required for implementing and maintaining AI solutions is a limiting factor to expand, despite potential long-term cost savings. India's public health expenditure is 1.9% of GDP, still short of the 2.5% target.  

AI Healthcare Frameworks in India

Indian Council of Medical Research (ICMR) Guidelines (2023): Outlines 10 principles, including accountability, data privacy, and fairness, to ensure ethical AI use.

Digital Personal Data Protection Act (2023): Provides the fundamental rules for protecting health data privacy and security, and the Ayushman Bharat Digital Mission (ABDM) framework is designed to comply with it.

National Digital Health Blueprint (NDHB): Guiding the implementation of the ABDM. It promotes the use of open, interoperable, and standards-based digital systems.

National Digital Health Mission (NDHM): Creates unified health IDs to generate AI-ready data.

IndiaAI Initiative: Promotes AI ecosystem development, supporting startups and skilling programs.

Clinical Decision Support System (CDSS): An AI-powered CDSS is integrated into the national telemedicine platform, eSanjeevani, to assist doctors with diagnoses.

Tuberculosis elimination program: AI is used for screening and predicting outcomes in the TB program, contributing to a reduction in adverse outcomes in some regions.

National Strategy on Artificial Intelligence (NSAI): Initiatives by NITI Aayog have focused on leveraging AI for broader social and health benefits, including tackling issues related to health professional shortages and accessibility. 

Global collaboration: India joined the HealthAI Global Regulatory Network in September 2025 to align with international practices for overseeing AI in healthcare. 

Way Forward

Strengthen Infrastructure: Expand digital infrastructure via BharatNet and NDHM to support AI in rural areas. Example: Chhattisgarh’s solar-powered health centers ensure 24/7 electricity.   

Standardize Electronic Health Records (EHRs): Mandate and enforce a standardized EHR system across all hospitals and clinics, especially within the Ayushman Bharat Digital Mission (ABDM) framework.  

Develop India-Specific AI Models: Collaborate with IITs and startups to create AI models using diverse Indian datasets. Example: IIT-Delhi’s AI detectors for malaria and TB.  

Regulatory Sandbox: Establish a sandbox under ICMR to test AI solutions safely, modeled on RBI’s fintech sandbox.  

AI Education: Integrate AI modules into medical curricula and offer online courses for professionals.

Public Awareness: Launch campaigns like the Pulse Polio initiative to build trust in AI healthcare tools.  

Focus on low-cost, high-impact solutions: Prioritize the development of AI tools that can operate with limited connectivity and infrastructure, focus on high-impact use cases like predictive disease analytics and preventative medicine. 

Emphasize human oversight: Maintain a "human-in-the-loop" model for all critical clinical decisions involving AI, ensuring that a medical professional retains final accountability.

Global Collaboration: Leverage GRN and WHO’s Global Initiative on AI in Health for training and standards.  

Conclusion

India’s entry into the HealthAI GRN marks a step toward responsible AI integration in healthcare. By addressing infrastructure, data, and ethical challenges while leveraging global partnerships, India can enhance healthcare access and efficiency, aligning with its #AIforAll vision.  

Source: PIB