Testing the Limits of Language Models: A Conversational Framework for Medical AI Assessment

Large Language Models (LLMs) show promise for medical diagnosis, but traditional evaluations using static exam questions overlook the complexity of real-world clinical dialogues. We introduce a multi-agent conversational framework where doctor-AI and patient-AI agents interact to diagnose medical conditions, evaluated by a grader-AI agent and medical experts. We assessed the diagnostic accuracy of GPT-4 and GPT-3.5, in conversational versus static settings using 140 cases focusing on skin diseases. Our study revealed a decline in diagnostic accuracy, unmasking key limitations in LLMs’ ability to integrate details from conversational interactions to improve diagnostic performance. We introduced Conversational Summarization, a technique that enhanced performance, and expert review identified deficiencies compared to human dermatologists in comprehensive history gathering, appropriate use of terminology, and reliability. Our findings advocate for nuanced, rigorous evaluation of LLMs before clinical integration, and our framework represents a significant advancement toward responsible testing methodologies in medicine. ### Competing Interest Statement D.I.S. is the co-founder of FixMySkin Healing Balms, a shareholder in Appiell Inc., and a consultant with LuminDx. R.D. reported receiving personal fees from DWA, personal fees from Pfizer, personal fees from L'Oreal, personal fees from VisualDx, stock options from MDAlgorithms and Revea outside the submitted work, and a patent for TrueImage pending. ### Funding Statement No external funding was obtained for this study. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the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