Systematic review assesses automated feedback effectiveness of LLMs in medical imaging education, suggesting improved training outcomes.
Background Large Language Models (LLMs) represent an ever-emerging and rapidly evolving generative artificial intelligence (AI) modality with promising developments in the field of medical education. LLMs can provide automated feedback services to medical trainees (i.e. medical students, residents, fellows, etc.) and possibly serve a role in medical imaging education. Aim This systematic review aims to comprehensively explore the current applications and educational outcomes of LLMs in providing automated feedback on medical imaging reports. Methods This study employs a comprehensive systematic review strategy, involving an extensive search of the literature (Pubmed, Scopus, Embase, and Cochrane), data extraction, and synthesis of the data. Conclusion This systematic review will highlight the best practices of LLM use in automated feedback of medical imaging reports and guide further development of these models.
No takes yet. Share an insight, caveat, or question.
Al-Mashhadani et al. (2025) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: