Grounding Clinical AI Competency in Human Cognition Through the Clinical World Model and Skill-Mix Framework
2026-04-09 • Artificial Intelligence
Artificial IntelligenceHuman-Computer Interaction
AI summaryⓘ
The authors explain that for clinical AI to work well, it needs a detailed model of the healthcare world, which doesn't currently exist. They created the Clinical World Model, which sees healthcare as interactions between the Patient, Provider, and Ecosystem. The authors describe how decision-making happens for all agents (humans and AI) and define eight key areas that show different AI skills in clinical settings. They highlight that success in one area doesn't guarantee success in others, emphasizing the complex nature of evaluating clinical AI. This model helps everyone understand exactly where and how AI is reliable in healthcare.
Clinical AIClinical World ModelPatient-Provider-EcosystemClinical cognitionDecision-making architecturesAI competencyValidationClinical settingsAuthority assignmentSkill-mix
Authors
Seyed Amir Ahmad Safavi-Naini, Elahe Meftah, Josh Mohess, Pooya Mohammadi Kazaj, Georgios Siontis, Zahra Atf, Peter R. Lewis, Mauricio Reyes, Girish Nadkarni, Roland Wiest, Stephan Windecker, Christoph Grani, Ali Soroush, Isaac Shiri
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.