Grounding Clinical AI Competency in Human Cognition Through the Clinical World Model and Skill-Mix Framework
Organizations: Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland · Department of Digital Medicine, Bern University Hospital, University of Bern, Bern, Switzerland · Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, United States · Clinical Research Development Center, Amir Oncology Teaching Hospital, Shiraz University of Medical Sciences, Shiraz, Iran ·
Abstract
The competency of any intelligent agent is bounded by its formal account of the world in which it operates. Clinical AI lacks such an account. Existing frameworks address evaluation, regulation, or system design in isolation, without a shared model of the clinical world to connect them. We introduce the Clinical World Model, a framework that formalizes care as a tripartite interaction among Patient, Provider, and Ecosystem. To formalize how any agent, whether human or artificial, transforms information into clinical action, we develop parallel decision-making architectures for providers, patients, and AI agents, grounded in validated principles of clinical cognition. The Clinical AI Skill-Mix operationalizes competency through eight dimensions. Five define the clinical competency space (condition, phase, care setting, provider role, and task) and three specify how AI engages human reasoning (assigned authority, agent facing, and anchoring layer). The combinatorial product of these dimensions yields a space of billions of distinct competency coordinates. A central structural implication is that validation within one coordinate provides minimal evidence for performance in another, rendering the competency space irreducible. The framework supplies a common grammar through which clinical AI can be specified, evaluated, and bounded across stakeholders. By making this structure explicit, the Clinical World Model reframes the field's central question from whether AI works to in which competency coordinates reliability has been demonstrated, and for whom.
Human-AI Decision Making
Human-AI decision making studies how people and AI systems share, support, or delegate decisions across tasks and institutions.
See all 173 papers