The Legal Positioning and Medical Malpractice Liability Regulation of Diagnosis AI

AI diagnoses, prevents, and treats diseases using machine and deep learning. The “hardware-software integrated” intelligent system efficiently processes data to improve clinical decision-making. This is shown by improved primary care diagnosis and treatment precision, optimized medical resource geographical allocation, and reduced human diagnostic errors. Medical disputes in clinical applications have increased due to “black box” algorithms, multi-party accountability framework for development, deployment, and administration, and legal classification disputes. These disagreements involve algorithmic and misuse-related misdiagnoses. Human medical liability frameworks face AI-driven autonomous judgment causation and culpability issues. Uncertain status, inadequate liability, and patient rights need systematic solutions. This article examines diagnostic AI legal regulation. This detailed comparison contrasts “service theory,” “product theory,” and “independent entity theory.” A “modified product theory,” supported by case law, classifies Diagnosis AI as a product and differentiates primary liability, establishing a liability assessment basis. Explain “data dependency and algorithmic autonomy,” then distinguish perceptual, cognitive, and behavioral operations. By empirically analyzing major cases to prove algorithmic flaws, improper use, and managerial oversights as fundamental causal factors in three negligence categories A multi-stakeholder liability structure includes hospitals, healthcare providers, and device manufacturers. Proof liability with AI category-specific 3D rules. These channels include law, judiciary and industry monitoring. By balancing medical technological innovation and patients' rights to life and health while promoting the regulated development of diagnostic and therapeutic AI using China's legal system and comparative law, this study supports AI-related medical harm adjudication challenges.