An international team of researchers, clinicians, educators, and patient partners has developed and validated the Health CARE-AI Framework-a consensus-backed roadmap designed to move AI governance from abstract principles to concrete professional competencies. The framework, published in JMIR Medical Education, was shaped by a modified Delphi process involving 303 participants and received near-unanimous endorsement, with 96% agreeing it clearly defines professionalism expectations for AI.
While the World Health Organization and UNESCO have established broad ethical principles for AI, clinicians and educators often lack clear direction when navigating patient privacy, algorithmic bias, or data stewardship. The Health CARE-AI initiative provides an actionable roadmap for responsible AI for Healthcare, translating values into teachable and institutional practices.
Four domains and ten core principles
The framework organizes ten core principles into four interconnected domains:
- Values: Establishing AI use as a shared duty with 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 clinical and educational decision-making.
- Accountability: Treating AI as a present "third party" during interactions, adhering to legal, privacy, and consent boundaries, and practicing ethical data stewardship.
- Structural Equity: Actively identifying and mitigating algorithmic bias, embedding equity into AI design through co-design with affected communities, and advancing environmental and workforce sustainability.
Near-unanimous expert consensus
The framework was developed through a 3-phase modified Delphi process that engaged 303 international participants. 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." This level of agreement underscores the readiness of the healthcare community to adopt structured, competency-based AI governance.
Companion toolkit for practical application
The Health CARE-AI Framework includes a companion implementation guide and toolkit with scenario-based applications for teaching, research, and governance. Medical schools, residency programs, and health systems can use these resources to evaluate readiness, update curricula, and audit AI deployments. The toolkit is designed to help organizations move from principle to practice without ambiguity.
Why this matters for healthcare professionals
The Health CARE-AI Framework provides a validated structure that healthcare professionals can use to assess their own AI literacy, uphold patient trust, and advocate for equitable AI deployment. The included toolkit offers direct, role-based scenarios that let clinicians and educators measure their readiness and address gaps in accountability. By embedding equity and sustainability into AI governance, the framework also equips professionals to push back against tools that risk deepening health disparities.
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