Liability in AI-Enabled Clinical Decision Support: Toward a Tiered Responsibility Model

Abstract

AI-enabled clinical decision support (CDS) is increasingly embedded in diagnosis and care pathways, yet liability remains unclear when recommendations cause harm. This paper offers a structured, actionable approach. We synthesize liability regimes across the United States, European Union, and Asia-Pacific and highlight gaps specific to adaptive, software-driven systems. We ground the analysis in ethical risks-accountability shifts, automation bias, and power asymmetries-that should shape how responsibility is shared. We then introduce a quantitative Tiered Liability Model that allocates total harm among developers, physicians, and hospitals using policy weights and three measurable indices: developer culpability, physician oversight, and AI explainability. We prove basic feasibility and monotonicity properties of the allocation, explain how to operationalize the indices with audits, EHR-based oversight signals, and logging/traceability, and provide a regulatory crosswalk that maps regional doctrines to these indices. Two scenario analyses-a pneumonia misdiagnosis and a hospital operations system that contributes to understaffing-illustrate how the model yields fair, incentive-aligned allocations and how improvements in explainability and oversight shift liability. To support adoption, we include a ready-to-use procurement scorecard with measurable evidence and pass/fail gates, together with an implementation roadmap and regulatory levers (metrics standardization, safe harbors, post-market surveillance). The result is a coherent framework that protects patients, sustains innovation, and gives regulators, providers, and vendors a common language for assigning responsibility.

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