Legal and Ethical Governance of Artificial Intelligence in Radiology: A Narrative Transcontinental Review Across Pan-Africa, Pan-Asia, and the Americas

Abstract Artificial intelligence (AI) is now embedded in radiology workflows for prioritization, interpretation, quantification, and reporting. As adoption expands, radiologists must address professional responsibility, liability, patient disclosure, data governance, and algorithmic bias. This narrative review used PRISMA-informed, non-systematic methods. PubMed/MEDLINE, Scopus, and Google Scholar were searched together with official regulatory and professional websites (including the World Health Organization, the Food and Drug Administration, the Department of Health and Human Services, the National Institute of Standards and Technology, the Organization for Economic Cooperation and Development, the European Commission, Indian Council of Medical Research, MeitY, the Association of Southeast Asian Nations, and AU/AUDA-NEPAD) for English-language literature and legal or policy documents published from January 2018 to March 2026. Search terms combined “radiology,” “artificial intelligence,” “ethics,” “liability,” “privacy,” “bias,” “governance,” “HIPAA,” “GDPR,” “DPDP,” and related terms. Review findings: global principles are converging, but implementation remains regionally contingent. We compare African, Asian, American, and selected international benchmarks, clarify that AFRI-CARE, ASIA-GUIDE, AMERICAS, and RADIANT are proposed conceptual frameworks, and show how they translate shared governance domains into practice. Defensible AI adoption in radiology requires transparent methods, local validation, subgroup monitoring, human oversight, and context-sensitive governance that reflects infrastructure, law, and equity priorities across regions.