India AI Impact Summit 2026

unlocking-health-equity-through-responsible-ai

2 min read

TRACK:Safe and Trusted AI
SPEAKERS
  • Dr. Ayesha Chaudhary, WomenLift Health
  • Dr. B. Srinivas, MoHFW & National Medical Library
  • Dr. Jan Herzhoff, Elsevier
  • Dr. Sangeeta Reddy, Apollo Hospitals
  • Mr. Kazim Rizvi, CoRE-AI
SESSION OVERVIEW

This session will examine how responsible AI can advance health equity by improving access to trusted medical knowledge, clinical decision support, and workforce capacity. Bringing together policymakers, healthcare leaders, clinicians, and industry experts, the discussion will focus on trust, transparency, and governance in health AI. The panel will explore how evidence-based, explainable AI systems can be deployed safely and at scale to strengthen health systems and improve outcomes, particularly in emerging and resource-constrained settings.

VIDEO RECORDING

Unlocking Health Equity through Responsible AI

Abstract

The panel explored how responsible artificial intelligence can be harnessed to improve health equity, especially in resource‑constrained settings. Topics covered included the core principles of trustworthy AI (explainability, bias avoidance, privacy, accountability), government‑led digital‑learning initiatives for medical students, challenges of applying AI in a probabilistic clinical environment, gender and geographic disparities, and the need to embed compassion into AI‑enabled care. The discussion highlighted concrete programmes such as the “One Nation, One Subscription” digital library rollout and emphasized that AI should augment—not replace—human clinicians while being governed by clear ethical standards.

Key Takeaways

  • Explainability, bias mitigation, privacy, accountability, and equity are identified as the five foundational pillars for trustworthy health AI.
  • Bias can originate at the query‑formulation stage; careful prompting and inclusive question design are essential.
  • India’s government is scaling digital medical education through the ONOS subscription and a pilot in 57 government colleges, with plans to reach ≈800 colleges and later private institutions.
  • Responsible AI is critical when disseminating digital content to ensure that unregulated or inaccurate information does not harm learners.
  • Medical AI must respect the probabilistic nature of medicine; clinicians need tools that narrow diagnostic or therapeutic options rather than present overly broad lists.
  • Data quality and causal reasoning are prerequisite for AI systems to be clinically actionable.
  • Gender and geographic disparities persist; responsible AI policies must explicitly address connectivity and cultural relevance to close these gaps.
  • Compassion remains a core, non‑technical component of care; AI should be leveraged to enhance—not replace—human empathy.
  • The panel advocates a “move fast but cautiously” development ethos, embedding the nine‑principle AI ethics framework throughout design, deployment, and monitoring.
  • Future directions include expanding tele‑education, integrating AI‑driven decision support in primary‑care settings, and continuously revisiting governance structures to keep pace with technological advances.

See Also:

Graph View