Principles for Responsible AI in Health Professions Education, Research, and Care: Health CARE-AI (Contextual, Accountable, Responsible, and Equitable Artificial Intelligence) Framework Delphi Consensus Study.

BACKGROUND: Artificial intelligence (AI) is rapidly integrating into health professions education and clinical practice, creating significant opportunities alongside new ethical challenges. Although current international and professional guidance establishes essential values, it offers limited direction for how clinicians, educators, learners, and institutions should act in routine educational, research, and clinical contexts. The CARE-AI (Contextual, Accountable, Responsible, and Equitable Artificial Intelligence) project responds to this practice-level gap by articulating guidance that moves beyond values toward professional accountability and equity, with explicit attention to educational, research, and clinical practice contexts. OBJECTIVE: The study objective was to develop and validate a consensus-based, actionable framework of principles to guide responsible AI use across health professions education, research, and clinical care. METHODS: We conducted a 3-phase modified Delphi consensus study, reported in accordance with the Accurate Consensus Reporting Document. Phase 1 involved 2 international professional meetings and 3 purposively sampled focus groups (AI or technology, health professions education, and ethics or professionalism) to adapt and refine draft principles using an exploratory qualitative approach. Phase 2 used an online survey with a 5-point importance scale and prespecified consensus criteria (inclusion ≥70%: high ratings; exclusion ≥70%: low ratings). Phase 3 used include, exclude, or undecided voting on revised principles. Quantitative thresholds determined consensus. Qualitative free-text comments informed iterative refinement. RESULTS: Participants represented diverse communities of practice across health professions education, clinical care, patient partners, ethics, and digital health, spanning multiple professional roles and training levels. Across all phases, 303 unique participants contributed to the study. Phase 1 focus groups (n=61) provided early insight and direction. In phase 2, the first Delphi survey round, 242 participants initiated the survey, with 120 (49.6%) participants completing it. In phase 3, the second Delphi survey round, 103 participants were invited based on expressed interest at the end of the first round; 78 participants initiated the survey and 75 completed it (75/78, 96.2% of starters). In phase 2, of the 61 statements, 58 (95%) met the inclusion criteria, and participants submitted 1887 comments (697 were content rich), prompting clearer accountability language, stronger equity commitments, and more usable wording. In phase 3, all 10 principles and their statements met the inclusion criteria. Participants contributed 224 comments (179 were content rich) that informed final refinements. Endorsement was near unanimous: 96% (72/75) agreed or strongly agreed that the framework clearly defined professionalism expectations for AI to meet educational, technological, and ethical needs in the health professions. CONCLUSIONS: The Health CARE-AI Framework, with its preamble and 10 principles, articulates actionable, consensus-validated guidance that moves from values to competence, into professional accountability, and toward structural commitments to equity. Paired with a companion implementation guide and toolkit, the framework is intended to support use across education, research, and clinical settings.