Discuss the potential and limitations of Artificial Intelligence in bridging India's healthcare access gap. Refer to recent government initiatives.

India's healthcare access gap is essentially a delivery gap — medical knowledge exists, but the clinicians and institutions to carry it to the last mile do not. Artificial Intelligence, by extending expert judgement to non-specialist settings, offers a partial but significant corrective, provided its risks are governed alongside its adoption.

Potential: extending scarce expertise

• Screening without specialists: AI chest X-ray tools such as DeepCXR flag presumptive tuberculosis cases and are deployed across eight States/UTs at no cost to government, enabling non-specialist screening [1].
• Decision support at the periphery: the eSanjeevani telemedicine platform supported around 282 million consultations (April 2023–November 2025), with AI-assisted diagnosis benefiting roughly 12 million patients [1].
• Data backbone: Ayushman Bharat Digital Mission — about 799 million health IDs and 671 million linked records (August 2025) — supplies the interoperable base AI models need [1].
• Institutional capacity: Centres of Excellence at AIIMS Delhi, PGIMER Chandigarh and AIIMS Rishikesh anchor indigenous solution development [1].
• Administrative efficiency: automation of documentation and hospital revenue-cycle work frees clinician time for patients.

Limitations: trust, equity and governance

• Bias and validation gaps: models trained on unrepresentative data may misperform on rural and tribal populations; WHO's guidance on large multi-modal models issues over 40 recommendations precisely on bias, accountability and human oversight [2].
• Privacy and consent: large-scale health-record linkage raises data-protection concerns.
• A new digital divide: connectivity and device gaps may concentrate benefits where access is already better.
• Accountability: liability for algorithmic error in clinical decisions remains unsettled.

Government response acknowledges this duality: SAHI (Strategy for AI in Healthcare for India) and BODH, a privacy-preserving benchmarking platform built by IIT Kanpur with the National Health Authority, were launched at the India AI Impact Summit 2026 [3][4].

AI is therefore an amplifier of health systems, not a substitute for them. Its promise will be realised only if validation, equity safeguards and human oversight advance in step with deployment — the path SAHI charts, and one consistent with Article 21's guarantee of health and SDG-3's goal of universal health coverage.