Algorithmic Error and Medical Negligence in India: Navigating the Regulatory Vacuum in AI-Driven Healthcare

The malfunctioning of AI-enabled healthcare systems exposes a pronounced structural gap within India’s medico-legal framework, wherein established doctrines of medical negligence struggle to accommodate automated decision-making. Indian law, historically grounded in human-centric notions of duty of care, standard of care, and proximate causation, does not clearly attribute liability when diagnostic or therapeutic errors arise from machine-learning systems—particularly in complex, multi-stakeholder environments involving clinicians, hospitals, software developers, and data providers. Existing legal frameworks, including the Consumer Protection Act, 2019, and negligence principles shaped by the Indian Medical Council and judicial precedents, remain largely ill-suited to opaque and adaptive AI systems, whose functioning often resists conventional tests of foreseeability. This regulatory inadequacy is compounded by the absence of a dedicated AI liability regime and the fragmented interplay between statutes such as the Information Technology Act, 2000, the Digital Personal Data Protection Act, 2023, and sector-specific medical device and pharmaceutical regulations. The resulting legal uncertainty is particularly evident in questions of causation and evidentiary burden. Claimants face significant challenges in reconstructing algorithmic decision-making processes and in demonstrating deviation from an evolving, technology-informed standard of care. Comparative regulatory approaches—such as the U.S. Food and Drug Administration’s Software as a Medical Device (SaMD) framework and emerging European liability models—underscore the need for India to adopt mechanisms ensuring algorithmic transparency, mandatory error-reporting, and a calibrated liability structure that balances innovation with patient safety. Absent such reform, India risks entrenching a regulatory vacuum in which algorithmic errors are insufficiently deterred, accountability remains diffuse, and patients are left navigating an uncertain space between traditional medical malpractice and unregulated AI-driven harm.