CURE-AI: Contextualizing Uncertainty in Clinical Decision Making: A Conceptual Framework for Uncertainty-Aware AI Designs

Background/Objectives: Current clinical AI systems often report uncertainty as a static metric not tailored to the dynamic nature of clinical decision-making. To bridge the gap between computational variance and natural uncertainty in clinical practice, we propose the Contextual Uncertainty Reasoning for Artificial Intelligence (CURE-AI) framework. Methods: CURE-AI formalizes system uncertainty as a logical interaction between internal computational variance (the AI/ML pipeline) and external clinical ambiguity (the clinical workflow). The framework is constructed on five hierarchical levels of uncertainty resolution: (1) Clinical Uncertainty, (2) Computational Uncertainty, (3) Contextual Linkage, (4) Clinical Response, and (5) Human Outcome. Results: By mapping the framework to clinical workflows, we demonstrate that identical statistical uncertainty margins require different system recommendations depending on the patient context. CURE-AI translates computational variance into five actionable system behaviors: (1) inquiring for targeted missing data to resolve epistemic gaps, (2) accepting biological variability to prevent unnecessary testing, (3) scaling automated decisions or deferring to human judgment based on clinical risk, (4) suppressing AI output to adhere to mandated clinical protocols, and (5) recommending longitudinal patient reevaluation for prognostic uncertainty. Conclusions: CURE-AI recognizes AI uncertainty as an active workflow. By mapping computational variance to clinical action, the framework provides a conceptual blueprint for decision support systems that foster appropriate reliance and safeguard patient care.