While the World Health Organization (WHO) and UNESCO have established broad ethical principles for AI, clinicians and educators often lack clear direction when navigating patient privacy, algorithmic bias, student assessment, or data stewardship. To address these issues, an international team of researchers, clinicians, educators, ethicists, and patient partners has developed and validated the Health CARE-AI (Contextual, Accountable, Responsible, and Equitable Artificial Intelligence) Framework. 

Published in JMIR Medical Education, the study seeks to provide an actionable, consensus-backed roadmap for the responsible integration of AI across the healthcare learning and practice continuum.

Developed through a 3-phase modified Delphi consensus process involving 303 international participants, the framework received near-unanimous endorsement from experts. According to the study, 96% of participants agreed or strongly agreed that the framework clearly defines professionalism expectations for AI across educational, technological, and ethical needs.

The study highlights that AI is not simply an extension of existing digital or social media tools—it introduces fundamental challenges to clinical autonomy, relational trust, and health equity. The Health CARE-AI initiative was designed to move beyond abstract values into concrete competencies, professional accountability, and structural commitments to equity that can be directly taught, supervised, and institutionalized.

Reported in accordance with the ACCORD (Accurate Consensus Reporting Document) guidelines, the Health CARE-AI Framework organizes 10 core principles into 4 interconnected domains:
• Values: Establishing AI use as a shared individual and collective duty while ensuring transparency, honesty, and integrity in AI-assisted care and learning.
• Competence: Committing to continuous, role-appropriate AI literacy and maintaining critical human judgment—ensuring AI complements rather than replaces clinical and educational decision-making.
• Accountability: Treating AI as a present "third party" during interactions, adhering strictly to legal, privacy, and consent boundaries, and practicing ethical data stewardship.
• Structural Equity: Actively identifying and mitigating algorithmic bias, embedding equity into AI design and governance through co-design with affected communities, and advancing environmental and workforce sustainability.

The Health CARE-AI Framework is paired with a companion implementation guide and toolkit. The toolkit includes scenario-based applications across teaching, research, and governance, allowing medical schools, residency programs, and health system leaders to proactively evaluate their readiness, update curricula, and audit AI deployments.

As AI tools continue to redefine medical education and clinical workflows, the study says the Health CARE-AI Framework offers a timely, validated compass to safeguard patient welfare, protect relational trust, and foster structural equity across global health systems.