Principles for Responsible AI in Health Professions Education, Research, and Care: Health CARE-AI (Contextual,...

Area of Science:

• Medical Education
• Health Informatics
• Bioethics

Background:

• Artificial intelligence (AI) integration in healthcare presents ethical challenges and a need for practical guidance.
• Existing professional guidelines offer limited direction for AI use in routine educational, research, and clinical settings.
• The CARE-AI project addresses this gap by developing actionable principles for responsible AI implementation.

Purpose of the Study:

• To develop and validate a consensus-based framework for responsible AI use in health professions education, research, and clinical care.
• To translate AI values into professional accountability and equity across diverse healthcare contexts.

Main Methods:

• A 3-phase modified Delphi consensus study involving international experts and stakeholders.
• Phase 1: Qualitative refinement of draft principles via focus groups and meetings.
• Phases 2 & 3: Online surveys with quantitative consensus criteria and qualitative feedback for iterative refinement.

Main Results:

• 303 unique participants contributed across all study phases.
• Consensus was achieved on 10 core principles for the Health CARE-AI Framework.
• Near-unanimous endorsement (96%) indicated the framework meets educational, technological, and ethical needs for AI in health professions.

Conclusions:

• The Health CARE-AI Framework provides actionable, consensus-validated guidance for responsible AI integration.
• The framework emphasizes competence, professional accountability, and structural equity in AI use.
• A companion implementation guide and toolkit support framework adoption in education, research, and clinical settings.