What question did this study set out to answer?

The research aims to create a comprehensive governance framework for clinical artificial intelligence systems, addressing potential failures and oversight requirements.

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February 5, 2026Open Access

Governing Adaptive Clinical Artificial Intelligence: Structural Failure Modes, Auditability, and Infrastructure for Decision Safety

Key Points

The research aims to create a comprehensive governance framework for clinical artificial intelligence systems, addressing potential failures and oversight requirements.
Synthesis of multiple manuscripts on adaptive clinical AI governance
Review of governance-induced failure modes and institutional constraints
Development of guidelines for auditability and decision safety
Identifies key failure modes in adaptive AI systems
Highlights the need for increased oversight and governance structures
Suggests strategies for ensuring decision safety in clinical settings

Abstract

This framework serves as the program-level synthesis and research roadmap for a multi-manuscript agenda in clinical artificial intelligence governance. Constituent works include peer-reviewed journal articles, publicly archived preprints, and working papers addressing complementary aspects of governance-induced failure modes, adaptive oversight, and institutional constraint. The intended audience includes clinicians, health informaticians, machine learning researchers, regulators, policymakers, and institutional leaders responsible for the deployment and oversight of adaptive AI systems in healthcare.

Governing Adaptive Clinical Artificial Intelligence: Structural Failure Modes, Auditability, and Infrastructure for Decision Safety

Key Points

Abstract

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