Evaluation of AI Large Models for Diagnosis and Treatment in Real-World Cases: Multicenter Retrospective Study

De-identified inpatient medical records were retrospectively collected from the urology departments of four tertiary hospitals (200 cases per site, 800 in total). Each case included standardized clinical information such as demographics, chief complaint, history of present illness, past medical history, physical examination, laboratory and imaging findings, discharge diagnosis and treatment plan. To simulate the role of an AI system in a "first-visit physician" scenario, all diagnostic conclusions, differential diagnoses and treatment plans were removed before being input into the models. Three large language models (ChatGPT, Gemini and DeepSeek) were prompted with a standardized instruction: "Based on the above clinical information, provide your preliminary diagnosis, differential diagnoses and treatment recommendations." Each model generated outputs including (i) primary and secondary diagnoses, (ii) differential diagnosis lists with reasoning and (iii) preliminary treatment suggesti