The integration of artificial intelligence (AI) assisted diagnosis and treatment has ushered in a new era of healthcare, but it also presents significant challenges to traditional theories of causation in medical malpractice liability. This paper examines the limitations of conventional causation determination methods in the context of AI-assisted diagnosis and proposes a shift toward probabilistic causation theory. We construct a rule system centered on “probability gain” supported by apparent cause identification, joint causation identification, spurious cause analysis, and causal chain reconstruction. This approach assists courts in determining causation when evidentiary proof is limited and causation is uncertain. We demonstrate how this framework can clarify the roles of medical personnel and AI entities in mixed causation cases and determine the attributable causal power and liability share in joint causation scenarios. Our findings suggest that probabilistic causation theory offers a promising avenue for unraveling the complex causation puzzles in AI-assisted medical malpractice cases, potentially reshaping the legal landscape for healthcare liability in the age of artificial intelligence.