Legal Accountability for AI Errors in Life-Saving Medical Decisions: Medical Negligence, Liability and Data Protection under Indian Law

Abstract

Artificial Intelligence (AI) is revolutionizing the healthcare industry at a very fast pace by increasing diagnostic accuracy, foretelling treatment outcomes, and helping in life-saving healthcare decisions, yet its implementation brings important legal, ethical, and regulatory issues into play when something goes wrong. Classic medical negligence principles, as formulated by Bolam v. Friern Hospital Management Committee and polished by Bolitho v. City and Hackney Health Authority, centre upon the behaviour of medical practitioners, but their application is cumbersome whenever human judgment comes combined with AI-suggested options. Indian jurisprudence, especially that of Jacob Mathew v. State of Punjab, gives a basis for determining negligence but doesn't inherently tackle accountability in AI-supported judgments, creating a lack of clarity in the law. This research critically analyses the liability matrix of physicians, hospitals, AI programmers, and software providers, and the regulatory requirements imposed by the National Medical Commission, Consumer Protection Act, Indian Penal Code, Information Technology Act, and the Digital Personal Data Protection Act, 2023. This research also delves into difficulties in assessing causation in AI-supported treatment, the moral responsibilities of healthcare professionals in upholding patient autonomy, and the risks of data protection in processing sensitive health information for training algorithms. Taking comparative lessons from the U.S. Food and Drug Administration's regulation of Software as a Medical Device (SaMD) and the Indian Central Drugs Standard Control Organization (CDSCO) regime, the article calls for an overhauling need for an integrated AI healthcare liability legislation in India that balances
accountability with no stifling of innovation. Eventually, the study advocates for a legislative code requiring algorithmic transparency, AI-specific error reporting systems, and patient-oriented safeguards, thus reconciling technological progress with medical ethics, data protection, and patient safety in an increasingly digitizing healthcare environment.