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

Conventional artificial intelligence (AI) models can remain overconfident when their predictions are unreliable, limiting clinical adoption. Uncertainty quantification (UQ) supplements predictions with information about confidence, but the literature is often mathematically dense and difficult to translate into action. In this clinically oriented review, we synthesize current evidence and offer a practical perspective on the deployment of uncertainty-aware AI. We describe how uncertainty arises, summarize four principal computational approaches, and examine applications across clinical tasks, model development, foundation models, large language models, and agentic workflows. Uncertainty estimates may identify outputs requiring review, but they do not independently establish safety; their value depends on validation against relevant errors and linkage to appropriate actions in the intended setting. We, therefore, propose an adaptable roadmap connecting local reliability evaluation and threshold selection with subgroup assessment, prospective clinical evaluation, ongoing monitoring, and governance.