Impact of Multimodal Prompt Elements on Diagnostic Performance of GPT-4(V) in Challenging Brain MRI Cases

Background Recent studies have explored the application of multimodal large language models (LLMs) in radiological 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 GPT-4(V)-based brain MRI differential diagnosis. Methods Thirty brain MRI cases with a challenging yet verified diagnosis were selected. Seven prompt groups with variations of four input elements (image, image annotation, medical history, image description) were defined. For each MRI case and prompt group, three identical queries were performed using an LLM-based search engine (© PerplexityAI, powered by GPT-4(V)). Accuracy of LLM-generated differential diagnoses was rated using a binary and a numeric scoring system and analyzed using a chi-square test and a Kruskal-Wallis test. Results were corrected for false discovery rate employing the Benjamini-Hochberg procedure. Regression analyses were performed to determine the contribution of each individual input element to diagnostic performance. Results The prompt group containing an annotated image, medical history, and image description as input exhibited the highest diagnostic accuracy (67.8% correct responses). Significant differences were observed between prompt groups, especially between groups that contained the image description among their inputs, and those that did not. Regression analyses confirmed a large positive effect of the image description on diagnostic accuracy (p << 0.001), as well as a moderate positive effect of the medical history (p < 0.001). The presence of unannotated or annotated images had only minor or insignificant effects on diagnostic accuracy. Conclusion The textual description of radiological image findings was identified as the strongest contributor to performance of GPT-4(V) in brain MRI differential diagnosis, followed by the medical history. The unannotated or annotated image alone yielded very low diagnostic performance. These findings offer guidance on the effective utilization of multimodal LLMs in clinical practice. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study did not receive any funding ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics committee/IRB of the Technical University of Munich waived ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors