Application of Multimodal Large Language Models in Cutaneous Lesion Recognition of Talaromycosis and Cryptococcosis (Preprint)

BACKGROUND Talaromycosis and cryptococcosis are prevalent in Southern China and Southeast Asia and are frequently misclassified due to overlapping lesion morphology and limited access to confirmatory testing. OBJECTIVE To evaluate the zero-shot diagnostic performance of multimodal large language models in identifying and differentiating cutaneous lesions of talaromycosis and cryptococcosis METHODS Published clinical photographs of cutaneous lesions of talaromycosis and cryptococcosis were systematically retrieved up to 31 August 2025, and seven representative multimodal large language models were benchmarked under a strictly zero-shot setting using a standardized prompt template and a predefined output schema. Latency, unanswerable/invalid response rates, and diagnostic performance were evaluated using accuracy, precision, sensitivity, specificity, F1-score, and Matthews correlation coefficient. For explanation quality assessment, model-generated texts were independently rated by two clinicians across five dimensions, and hallucination events were quantified. RESULTS In total, 214 articles (95 for talaromycosis and 119 for cryptococcosis), including 244 talaromycosis cutaneous lesion images and 236 cryptococcosis cutaneous lesion images, were collected for zero-shot evaluation. Most models achieved acceptable performance recognition, among them, ChatGPT-5 achieved the best performance. For comprehensive performance comparison, ChatGPT-5 ranked first across six indicators but exhibited relatively lower sensitivity. Evaluation of the output text quality demonstrated that the diagnostic texts generated by GPT-5 were excellent. The EQI was 70.08, with a hallucination rate of 21.76%. CONCLUSIONS ChatGPT-5 demonstrates feasibility in the recognition of cutaneous lesions of talaromycosis and cryptococcosis under zero-shot conditions and can serve as a potential tool for assisting in the analysis of infectious skin disease images.