Human-in-the-loop governance of artificial intelligence in cardiology: From ethical principles to operational...

Artificial intelligence (AI) is transforming cardiology across ECG interpretation, imaging, risk prediction, remote monitoring, and workflow automation. Cardiologists need governance models that preserve clinical judgment, reduce harm, and reflect Indian realities.

This narrative reviews policy documents (2018-February 2026) spanning the EU AI Act, WHO, OECD, ICMR, FDA GMLP, FUTURE-AI, CHAI, Joint Commission, and MLOps frameworks, alongside medico-legal, automation-bias, fairness, and cardiology-AI implementation studies. We compared these instruments in human-in-the-loop (HITL) oversight and identify gaps in high-risk domains such as echocardiography AI, CT-FFR, cath-lab decision support, and wearable-based rhythm monitoring in India.

We propose practical HITL governance priorities for cardiology: local validation, calibrated alerting, explicit override pathways, bias surveillance, medico-legal accountability, and governance structures embedded within everyday cardiac workflows.

Realizing meaningful human-in-the-loop oversight requires investment in governance infrastructure, workforce development, transparent performance metrics, and learning systems treating AI-related incidents as opportunities for continuous improvement.