Background Studies have explored the application of multimodal large language models (LLMs) in radiologic differential diagnosis. Yet, how different multimodal input combinations affect diagnostic performance is not well understood. Purpose To evaluate the impact of varying multimodal input elements on the accuracy of OpenAI's GPT-4 with vision (GPT-4V)-based brain MRI differential diagnosis. Materials and Methods Sixty brain MRI cases with a challenging yet verified diagnosis were selected. Seven prompt groups with variations of four input elements (image without modifiers [I], annotation [A], medical history [H], and image description [D]) were defined. For each MRI case and prompt group, three identical queries were performed using an LLM-based search engine (Perplexity AI, powered by GPT-4V). The accuracy of LLM-generated differential diagnoses was rated using a binary and a numeric scoring system and analyzed using a χ² test and a Kruskal-Wallis test. Results were corrected for false-discovery rate with use of the Benjamini-Hochberg procedure. Regression analyses were performed to determine the contribution of each input element to diagnostic performance. Results The prompt group containing I, A, H, and D as input exhibited the highest diagnostic accuracy (124 of 180 responses [69%]). Significant differences were observed between prompt groups that contained D among their inputs and those that did not. Unannotated (I) (four of 180 responses [2.2%]) or annotated radiologic images alone (I and A) (two of 180 responses [1.1%]) yielded very low diagnostic accuracy. Regression analyses confirmed a large positive effect of D on diagnostic accuracy (odds ratio [OR], 68.03; P < .001), as well as a moderate positive effect of H (OR, 4.18; P < .001). Conclusion The textual description of radiologic image findings was identified as the strongest contributor to the performance of GPT-4V in brain MRI differential diagnosis, followed by the medical history; unannotated or annotated images alone yielded very low diagnostic performance. © RSNA, 2025 Supplemental material is available for this article.

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