Moving beyond overconfidence: A clinical roadmap for integrating uncertainty-aware AI models into medical workflows.

Uncertainty-aware AI can flag unreliable outputs in clinical workflows, but uncertainty estimates alone do not establish safety without validation against relevant errors and linked actions.

• Why it matters: Conventional models may stay overconfident when wrong, and existing UQ guidance is often too mathematically dense to use operationally in medicine.
• What they did: This clinically oriented review synthesizes how uncertainty arises, summarizes four computational approaches, and surveys applications across clinical tasks, model development, foundation models, large language models, and agentic workflows.
• The result: It proposes an adaptable roadmap that connects local reliability testing and threshold selection with subgroup assessment, prospective clinical evaluation, ongoing monitoring, and governance.

AI summary of the abstract · may contain errors · not medical advice · read the paper.