Problem
Research questions and friction points this paper is trying to address.
Clinical AI
competency
world model
clinical cognition
skill-mix
Grounding Clinical AI Competency in Human Cognition Through the Clinical World Model and Skill-Mix Framework
🤖 AI Summary
This work addresses the fragmentation in evaluation, regulation, and system design of clinical AI stemming from the absence of a unified formal representation of the clinical world. The authors propose a “clinical world model” that conceptualizes medical practice as an interaction among patients, providers, and the healthcare ecosystem, establishing a shared human–machine decision-making architecture. They further introduce an eight-dimensional skill-composition framework to delineate the capability coordinates of clinical AI. Integrating clinical cognitive principles for the first time, this framework reveals that AI capabilities are not transferable across coordinates, reframing validity assessment as a reliability problem within specific coordinate contexts. The resulting irreducible capability space encompasses billions of unique coordinates, offering a unified language and structural foundation for the standardization, evaluation, and deployment of clinical AI systems.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Clinical World Model
Skill-Mix Framework
clinical cognition
AI competency
decision-making architecture
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