Large Language Model-Generated Differential Diagnoses in Radiology Education: Comparison with a Standard Casebook

Background/Objectives: Large language models (LLMs) are increasingly explored for radiology education, but their role in differential diagnosis learning remains under-investigated. This study evaluates the perceived usefulness of LLM-generated differential diagnoses compared with a standard radiology casebook. Methods: In this multi-center study, radiology trainees at junior (years 1-2) and advanced (years 3-5) levels evaluated 225 cases from a gold-standard casebook spanning nine subspecialties. Participants ranked the usefulness of their personal clinical experience, the casebook, and LLM teaching, and rated the LLM output using a five-point Likert scale across Clarity, Trust, Differential Usefulness, and Diagnostic Usefulness. Results: Thirteen trainees (4 junior, 9 advanced) completed 2425 evaluations. Overall, the casebook was rated most useful (mean rank 1.7 ± 0.2), followed by LLM teaching (1.8 ± 0.3) and personal experience (2.4 ± 0.2; p = 0.023), with no significant difference between LLM and Textbook (p = 0.438). Junior trainees favored LLM teaching more than advanced trainees (first-rank 66.6% vs. 22.1%; p = 0.037). Across subspecialties, the casebook consistently ranked highest, with LLM slightly lower and experience lowest. LLM teaching received high ratings for Clarity (4.4 ± 0.3), Trust (4.3 ± 0.3), Differential Usefulness (4.3 ± 0.4), and Diagnostic Usefulness (4.2 ± 0.4), with no statistically significant difference between domains (p = 0.149). Conclusions: LLM-generated differential diagnoses are clear, trustworthy, and perceived as highly useful for education, nearing the perceived value of a standard casebook, especially for junior trainees. While textbooks remain essential, LLMs hold promise as supplementary tools, but caution is needed due to potential inaccuracies and their inability to replicate image-based teaching.