Ask-Audit-Apply: A Framework for Clinical Reasoning with Artificial Intelligence.

Abstract

Artificial intelligence (AI)-enabled clinical decision support (CDS) tools are increasingly embedded in clinical workflows, generating and prioritizing clinical information, including diagnostic suggestions, evidence summaries, and management recommendations. Yet guidance on how clinicians should reason with AI at the point of care remains limited, creating risks of over-reliance, inconsistent verification, and diffusion of responsibility. We propose Ask-Audit-Apply (AAA) as a clinician-facing framework for AI-supported clinical reasoning that specifies decision-level behavioral practices. AAA integrates principles from evidence-based medicine, diagnostic reasoning, automation bias research, and emerging AI governance literature into a workflow-embedded process that preserves human judgment and accountability. The framework defines three behavioral obligations. Ask emphasizes deliberate formulation of the clinical question and decision context that shapes AI involvement. Audit requires critical evaluation of AI-generated recommendations, including evidentiary basis, applicability, and potential omissions, when available. Apply centers on integrating AI input with clinical expertise and patient values while maintaining ownership of the final decision. Although conceptual and not yet empirically validated, AAA articulates decision-level reasoning practices that may support education, supervision, and future research on safe AI integration across a range of CDS tools as these systems increasingly participate in clinical care.