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Driven by the large foundation models, the development of artificialintelligence has witnessed tremendous progress lately, leading to a surge ofgeneral interest from the public. In this study, we aim to assess theperformance of OpenAI's newest model, GPT-4V(ision), specifically in the realmof multimodal medical diagnosis. Our evaluation encompasses 17 human bodysystems, including Central Nervous System, Head and Neck, Cardiac, Chest,Hematology, Hepatobiliary, Gastrointestinal, Urogenital, Gynecology,Obstetrics, Breast, Musculoskeletal, Spine, Vascular, Oncology, Trauma,Pediatrics, with images taken from 8 modalities used in daily clinic routine,e.g., X-ray, Computed Tomography (CT), Magnetic Resonance Imaging (MRI),Positron Emission Tomography (PET), Digital Subtraction Angiography (DSA),Mammography, Ultrasound, and Pathology. We probe the GPT-4V's ability onmultiple clinical tasks with or without patent history provided, includingimaging modality and anatomy recognition, disease diagnosis, report generation,disease localisation.  Our observation shows that, while GPT-4V demonstrates proficiency indistinguishing between medical image modalities and anatomy, it facessignificant challenges in disease diagnosis and generating comprehensivereports. These findings underscore that while large multimodal models have madesignificant advancements in computer vision and natural language processing, itremains far from being used to effectively support real-world medicalapplications and clinical decision-making.  All images used in this report can be found inhttps://github.com/chaoyi-wu/GPT-4V_Medical_Evaluation.

Can GPT-4V(ision) Serve Medical Applications? Case Studies on GPT-4V for Multimodal Medical Diagnosis