Shifting The Burden: Apportioning Tortious Liability Between Physicians, Hospitals, And AI Developers For Diagnostic Errors

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Vijayaraghavan K, LLM, School of Criminal Law and Criminal Justice Administration, Tamil Nadu Dr. Ambedkar Law University, School of Excellence, Taramani, Chennai 600113, Tamil Nadu, India

ABSTRACT

The integration of Artificial Intelligence (AI) into clinical diagnostics has revolutionized the healthcare delivery system, yet there exist profound challenges to traditional medical malpractice frameworks. In the absence of the AI diagnosis the diagnostic errors were attributed to individual physician negligence or institutional systemic failures under the doctrine of respondeat superior. However, the opacity of "black-box" machine learning algorithms disrupts this binary paradigm. When an AI diagnostic tool generates a misdiagnosis, false positive, or false negative that results in patient harm, identifying the legally responsible party becomes a complex jurisdictional and doctrinal knot. As AI systems transition from passive assistive tools to autonomous diagnostic agents, traditional tort law frameworks face significant strain. The analysis evaluates how concepts of negligence, vicarious liability, and strict products liability apply to each stakeholder. It explores the "black box" problem of AI transparency, the evolving standard of care for physicians relying on algorithmic recommendations, and the institutional duties of hospitals in vetting and monitoring digital health technologies. The evolving standard of care for physicians argues that doctors face a dual risk: "automation bias," where they blindly rely on erroneous AI outputs, and "automation rejection," where they override accurate algorithmic insights. As the inquiry shifts to hospital the institution faces direct liability under corporate negligence doctrines which could arise due to inadequate procurement vetting, deficient staff training, and failure to implement algorithmic oversight protocols. In the case of AI developers, the traditional product liability frameworks such as specific manufacturing, design, and warning defects are evaluated in order to classify either as product or service. This article examines the complex triadic relationship between physicians, healthcare institutions, and AI developers when diagnostic errors occur. It also explores the shifting burdens of tortious liability among three primary stakeholders: the attending physician, the healthcare institution, and the AI software developer.

Keywords: Artificial intelligence, medical malpractice, tort liability, diagnostic errors, product liability, hospital negligence, standard of care.

183. SHIFTING THE BURDEN- APPORTIONING TORTIOUS LIABILITY BETWEEN PHYSICIANS, HOSPITALS, A

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