Evaluating LLM -- Generated Multimodal Diagnosis from Medical Images and Symptom Analysis

Large language models (LLMs) constitute a breakthrough state-of-the-art Artificial Intelligence technology which is rapidly evolving and promises to aid in medical diagnosis. However, the correctness and the accuracy of their returns has not yet been properly evaluated. In this work, we propose an LLM evaluation paradigm that incorporates two independent steps of a novel methodology, namely (1) multimodal LLM evaluation via structured interactions and (2) follow-up, domain-specific analysis based on data extracted via the previous interactions. Using this paradigm, (1) we evaluate the correctness and accuracy of LLM-generated medical diagnosis with publicly available multimodal multiple-choice questions(MCQs) in the domain of Pathology and (2) proceed to a systemic and comprehensive analysis of extracted results. We used GPT-4-Vision-Preview as the LLM to respond to complex, medical questions consisting of both images and text, and we explored a wide range of diseases, conditions, chemical compounds, and related entity types that are included in the vast knowledge domain of Pathology. GPT-4-Vision-Preview performed quite well, scoring approximately 84\% of correct diagnoses. Next, we further analyzed the findings of our work, following an analytical approach which included Image Metadata Analysis, Named Entity Recognition and Knowledge Graphs. Weaknesses of GPT-4-Vision-Preview were revealed on specific knowledge paths, leading to a further understanding of its shortcomings in specific areas. Our methodology and findings are not limited to the use of GPT-4-Vision-Preview, but a similar approach can be followed to evaluate the usefulness and accuracy of other LLMs and, thus, improve their use with further optimization.