JMIR Publications

ABSTRACT

Artificial intelligence (AI) is no longer confined to optional decision support; it is becoming a routine presence in clinical work, shaping diagnostic hypotheses, triage priorities, risk estimates, and documentation. Yet most educational responses still treat AI as a tool used by an individual clinician. This framing underestimates how AI reshapes the unit of practice: interprofessional teams. We propose the concept of AI-Expanded Interprofessional Practice (AI-IPC), where AI systems function as consequential participants in team cognition—not as moral agents, but as sources of recommendations, uncertainty, and constraints that reorganize communication, authority gradients, and accountability. Building on interprofessional education (IPE) theory and situated learning, we argue that “AI literacy” alone is insufficient; learners must be trained to coordinate human–AI–human collaboration in realistic clinical settings. We outline a pragmatic, theory-aligned approach for AI-Expanded IPE (AI-IPE): clarifying boundary conditions for AI participation, mapping AI-specific subcompetencies onto existing IPE frameworks, and evaluating performance at the level of team behaviors rather than knowledge recall. We further emphasize that implementation requires digital infrastructure (eg, AI-enabled simulation environments, audit-trace assessment tools), institutional governance (eg, documentation and override protocols), and educator capacity that bridges AI, clinical workflow design, and IPE facilitation. AI will not replace interprofessional collaboration; it will change what collaboration requires. Education should make that change teachable.