Responsibility Definition and Risk Management in the Clinical Application of Medical Artificial Intelligence:A Review Based on Four-Level Classification

Abstract

The rapid penetration of medical artificial intelligence (MAI) into clinical diagnosis and treatment scenarios has reshaped the traditional medical service model. However, issues such as ambiguous responsibility definition and lagging risk management have severely constrained its safe and compliant development. This article uses the four-level AI classification system (tool-type, advisor-type, collaborative-type, autonomous-type) as the analytical framework to systematically review the responsibility definition logic and full-lifecycle risk management mechanisms for MAI clinical applications. The study finds that a multi-stakeholder responsibility system covering manufacturers, medical institutions, doctors, and regulatory authorities has been formed, along with a “prevention-control-remediation” risk management closed loop. However, deficiencies still exist in empirical validation, standard unification, adaptation to special scenarios, and coordination between technology and institutions. Future research should focus on directions such as dynamic responsibility quantification and matching, practical design of insurance pools, and development of lightweight management tools for primary care, providing theoretical support and practical references for the systematic governance of MAI clinical applications.