NMC Chairperson Urges Responsible AI Use in Healthcare

According to The Economic Times and Press Trust of India, National Medical Commission chairperson Abhijat Sheth addressed the national conference HealthAIcon 2026 and said "We are not just adopting AI, we are adopting it at scale across a diverse healthcare system, and that brings both opportunity and responsibility." Reporting by The Economic Times quotes Sheth saying medical education must reflect AI's clinical role: "Our education system must accept and reflect that reality. And certainly, that gives us more responsibility to work on regulatory issues related to AI." The Economic Times also quotes him: "This is not about turning a doctor into a technologist. It is about ensuring that every doctor understands what AI can and can't do, can interpret AI output critically, use AI safely in clinical practice, and maintain independent clinical judgement." MedicalDialogues earlier reported related NMC statements on integrating AI and digital healthcare into medical education standards and curriculum initiatives.

Industry-pattern observations: Healthcare AI adoption has shifted from pilots to operational deployments in imaging, triage, and decision support, creating a bigger need for clinician-level AI literacy, model limitations awareness, and interpretability tooling. For practitioners, that typically means increased demand for curricula covering model validation, data provenance, performance drift monitoring, and human-in-the-loop workflows rather than only algorithm mechanics.

Editorial analysis: A national regulator publicly prioritizing AI literacy frames AI as a systems-level concern, not just a vendor or hospital IT issue. When regulatory bodies emphasise education and regulatory work, industry stakeholders, medical schools, professional boards, continuing medical education providers, and hospital quality teams, commonly reassess accreditation criteria, examination content, and clinician credentialing to include AI-related competencies.

Editorial analysis: Observers should track whether the NMC or the National Board of Examinations publishes concrete competency frameworks, learning objectives, or guidance documents for integrating AI into undergraduate and postgraduate medical curricula. Other indicators include revisions to licensing exam content, the rollout of accredited AI-focused continuing medical education modules, and partnerships between medical colleges and technology vendors for supervised clinical validation studies.

Editorial analysis: Clinicians and healthcare data teams will likely need to prioritise interoperability, explainability, and local validation processes. Training programs that teach how to interpret model outputs, detect failure modes, and retain independent clinical judgement will be more relevant for frontline clinicians and for teams responsible for procurement and governance of AI tools.