Medical AI liability framework proposed, balancing physician and AI accuracy

A new paper explores liability frameworks for medical AI, proposing a simple, uniform liability level for physicians deviating from standard care, which can achieve optimal outcomes despite physician heterogeneity and private quality information. The study reveals a non-monotonic relationship between AI accuracy and optimal liability, suggesting that improved AI does not always lead to relaxed liability. Welfare loss due to information asymmetry is also analyzed, showing it depends on the reliability of standard care and AI accuracy, following an inverted-U pattern. AI

IMPACT Proposes a novel liability structure for medical AI that could influence regulatory approaches and physician adoption.

RANK_REASON Academic paper analyzing a theoretical framework for AI liability. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• AI recommendation
• personalized judgment-based treatment
• physician
• social planner
• standard of care
• Standard treatment

• paper
• policy

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