A Critical Narrative Synthesis Toward Precision Medical Training in Radiology Education

Objectives The research examines the intersection of artificial intelligence (AI), including large language models (LLMs), generative platforms, and adaptive simulations, with radiology education as a testing ground for precision learning, curricular creativity, faculty preparation, and governance. The aims here are not to catalog all of the uses, but to critically examine convergent findings and to comment on the mechanisms by which AI enhances, or subtracts from, diagnostic training. Methods Critical narrative synthesis guided by design-oriented perspective with abductive coding that integrated theory-driven and inductive approaches. Evidence was extracted using iterative peer-reviewed database searching and tracing of review-worthy reviews, studies, and society guidelines. Sources were kept that discussed AI application in radiology teaching and presented mechanisms of effect, recorded results, or protection measures against irresponsible adoption. Findings Three common mechanisms across various studies emerge: adaptive case curation that broadens exposure to underrepresentd pathology; iterative feedback loops that move assessment from episodic to longitudinal; and language scaffolding that improves report clarity and professional communication. Large language models apply these mechanisms further to curriculum design, interactive teaching, and assessment design, while gamification and simulation provide depth of experience and fun. Those advantages come with risks of overdependence, diagnostic deskilling, bias, and misinformation that are significant with low faculty readiness or lack of structures of oversight. Protections of human-in-the-loop frameworks, alternating between AI-assistance and AI-restricted interpretation, and redesign of assessments to reward independent reasoning are always urged. Curricular adoption worldwide is uneven, with interest among trainees frequently exceeding that of faculty, and with an associated generational divide. Professional organizations released initial recommendations, but agreement on standards of readiness and mechanisms of governance is partial. Conclusions Radiology exemplifies that with AI, precision medical education can be taken to the next level, but the gains depend on design. The AI should be integrated not to replace expertise but to facilitate intentional practice, critical thought, and access equity. The integration provides design postulations, research agendas, and practice-focused implications to inform responsible adoption, and radiology can both act as a template and an exemplar case to apply to wider medical education.