Performance of Multimodal GPT-4V on USMLE with Image: Potential for Imaging Diagnostic Support with Explanations

作者：Zhichao Yang, Zonghai Yao, Mahbuba Tasmin, Parth Vashisht, Won Seok Jang, Feiyun Ouyang, Beining Wang, Dan R. Berlowitz, Hong Yu · 发表于：medRxiv · 年份：2023 · DOI：10.1101/2023.10.26.23297629 · 被引用次数：38 · 研究领域：Artificial Intelligence in Healthcare and Education、COVID-19 diagnosis using AI、Radiomics and Machine Learning in Medical Imaging

Abstract Background Using artificial intelligence (AI) to help clinical diagnoses has been an active research topic for more than six decades. Past research, however, has not had the scale and accuracy for use in clinical decision making. The power of AI in large language model (LLM)-related technologies may be changing this. In this study, we evaluated the performance and interpretability of Generative Pre-trained Transformer 4 Vision (GPT-4V), a multimodal LLM, on medical licensing examination questions with images. Methods We used three sets of multiple-choice questions with images from the United States Medical Licensing Examination (USMLE), the USMLE question bank for medical students with different difficulty level (AMBOSS), and the Diagnostic Radiology Qualifying Core Exam (DRQCE) to test GPT-4V’s accuracy and explanation quality. We compared GPT-4V with two state-of-the-art LLMs, GPT-4 and ChatGPT. We also assessed the preference and feedback of healthcare professionals on GPT-4V’s explanations. We presented a case scenario on how GPT-4V can be used for clinical decision support. Results GPT-4V outperformed ChatGPT (58.4%) and GPT4 (83.6%) to pass the full USMLE exam with an overall accuracy of 90.7%. In comparison, the passing threshold was 60% for medical students. For questions with images, GPT-4V achieved a performance that was equivalent to the 70th - 80th percentile with AMBOSS medical students, with accuracies of 86.2%, 73.1%, and 62.0% on USMLE, DRQCE, and AMB...