Recent Global Developments
AI is moving from research to clinical practice with accelerated
efforts worldwide. The World Health Organization (WHO) emphasizes
safe, equitable AI deployment with governance and transparency.
The U.S. FDA published draft guidance on AI/ML-enabled medical
devices, focusing on safety, monitoring, and real-world
performance (2025).
Validated AI applications include imaging, cardiology, pathology,
emergency workflows, and remote monitoring (e.g., AI diagnostic
stethoscopes, radiology triage).
Key Uses and Benefits of AI in Healthcare
Medical imaging: Faster reads and prioritization
for screening and triage.
Predictive analytics: Risk scoring for sepsis,
readmissions, and patient deterioration.
Drug discovery: Accelerated identification of
drug candidates and trial design.
Virtual assistants/chatbots: Patient triage,
medication reminders, and administrative automation.
Operational efficiency: Scheduling, supply chain
optimization, and billing automation.
AI in Healthcare — The Indian Context
India’s digital health transformation includes initiatives like
Ayushman Bharat Digital Mission (ABDM) which builds interoperable
digital health infrastructure (unique health IDs, digital
records).
Practical AI deployments in India include AI-driven radiology and
ophthalmology screening programs, telemedicine platforms using
NLP/chatbots for basic triage, and state-level pilots with
conversational agents for maternal and public health support.
Legal, Ethical, and Cultural Concerns in India
Data protection and privacy: Governed by Digital
Personal Data Protection Act (DPDP Act) 2023 and rules in
2024-2025 regulating consent and data transfers.
Informed consent and trust: Diversity and digital
literacy gaps require robust consent in local languages.
Bias and equity: Urban-centric datasets risk
underperformance for rural or low-resource populations.
Regulatory clarity and liability: Unclear
responsibility for AI-driven clinical errors among developers,
hospitals, and clinicians.
Practical Recommendations for India
Build representative datasets; use federated learning to protect
privacy and improve model generalizability.
Prioritize explainability and human-in-the-loop approaches; AI
should assist, not replace clinicians.
Strengthen consent and patient education in local languages with
opt-out options and transparency.
Implement continuous post-market monitoring to detect bias and
performance drift.
Ensure compliance with DPDP Act and health sector regulations;
embed privacy-by-design principles.
The Future: Next 5–10 Years
AI will evolve from task-specific tools to integrated clinical
workflows and population health platforms.
Expect more regulated AI devices with clear lifecycle oversight
(training, validation, monitoring).
Increased use of real-world evidence for model updating and
personalized medicine.
Expansion of low-cost, offline edge AI devices for rural screening
(eye, TB, maternal health).
Policy evolution to clarify liability, standardize validation
datasets, and enable safe cross-border data flow.
Conclusion
AI offers transformative potential for healthcare globally and in
India by increasing access and enabling precision medicine.
Achieving these benefits requires deliberate policy, culturally
sensitive approaches, data representativeness, and strong legal
protections to ensure trust, equity, and safety.