·The Hindu·15 marks·250–350 words

How can digital public infrastructure such as ABDM be leveraged to build a responsible AI ecosystem in Indian healthcare?

In this answer
  1. Data foundation for trustworthy models
  2. Validation and benchmarking as a public good
  3. Governance and standard-setting
  4. Equity and access

Digital public infrastructure (DPI) — open, interoperable, population-scale digital rails — offers India a route to embed safeguards into health AI rather than regulate it afterwards. The Ayushman Bharat Digital Mission (ABDM), implemented by the National Health Authority and having crossed 90 crore ABHA accounts [1], is the natural spine of such an ecosystem.

Data foundation for trustworthy models

  • ABDM's health registries and longitudinal records can supply diverse, representative datasets, countering the algorithmic bias WHO warns of in its 2024 guidance on large multi-modal models [2].
  • The Health Information Exchange–Consent Manager operationalises consent-based data sharing, aligning with the Digital Personal Data Protection Act, 2023 [3].

Validation and benchmarking as a public good

  • The NHA–IIT Kanpur MoU (2024) created federated learning pipelines and an open benchmarking platform for out-of-sample AI validation [4].
  • BODH, launched at the India AI Impact Summit 2026, is a privacy-preserving benchmarking platform evaluating models on real-world data without sharing underlying datasets [5].

Governance and standard-setting

  • SAHI, launched alongside BODH, provides a national framework for safe, ethical and inclusive AI adoption, covering data stewardship, deployment and monitoring [5].
  • DPI's open-API design lets the state mandate accuracy, transparency and grievance-redress standards as conditions of ecosystem access.

Equity and access

  • Interoperable records allow validated AI decision-support to reach rural and underserved districts, extending scarce specialist expertise to the point of care.
  • The 2021 NITI Aayog strategy envisaged exactly this federated, citizen-centric architecture [6].

Persisting challenges: uneven digitisation across states, data-quality gaps, cybersecurity risks, and unresolved liability for AI-assisted clinical error.

DPI thus converts AI governance from a policing problem into a design choice — trust is built into the rails themselves. India should pair ABDM with mandatory pre-deployment benchmarking, capacity-building for clinicians, and periodic audits under SAHI. Anchored in the right to health flowing from Article 21 and SDG-3, such an ecosystem can make Indian healthcare both intelligent and accountable.

Sources

  1. 1ABDM Crosses 90 Crore ABHA Accounts, PIBABHA scale; NHA as implementing body
  2. 2WHO, Ethics and Governance of AI for Health: Guidance on Large Multi-Modal Models (2024)bias, representative datasets, 40+ recommendations
  3. 3The Digital Personal Data Protection Act, 2023, PRS Legislative Researchconsent-based processing of personal data
  4. 4NHA and IIT Kanpur sign MoU for digital public goods for AI in Healthcare, PIBfederated learning and open benchmarking platform
  5. 5Shri J.P. Nadda Launches SAHI and BODH Initiatives at the India AI Impact Summit 2026, PIBSAHI governance framework; BODH privacy-preserving benchmarking
  6. 6National Digital Health Mission: Strategy Overview, NITI Aayog (2021)federated, citizen-centric digital health architecture

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