Evaluation of GPT Large Language Model Performance on RSNA 2023 Case of the Day Questions

医学 医学物理学 核医学
作者
Pritam Mukherjee,Benjamin Hou,Abhinav Suri,Yan Zhuang,Christopher Parnell,N. Lee,Oana Stroie,Ravi Jain,Kenneth C. Wang,Komal Sharma,Ronald M. Summers
出处
期刊:Radiology [Radiological Society of North America]
卷期号:313 (1)
标识
DOI:10.1148/radiol.240609
摘要

Background GPT-4V (GPT-4 with vision, ChatGPT; OpenAI) has shown impressive performance in several medical assessments. However, few studies have assessed its performance in interpreting radiologic images. Purpose To assess and compare the accuracy of GPT-4V in assessing radiologic cases with both images and textual context to that of radiologists and residents, to assess if GPT-4V assistance improves human accuracy, and to assess and compare the accuracy of GPT-4V with that of image-only or text-only inputs. Materials and Methods Seventy-two Case of the Day questions at the RSNA 2023 Annual Meeting were curated in this observer study. Answers from GPT-4V were obtained between November 26 and December 10, 2023, with the following inputs for each question: image only, text only, and both text and images. Five radiologists and three residents also answered the questions in an "open book" setting. For the artificial intelligence (AI)-assisted portion, the radiologists and residents were provided with the outputs of GPT-4V. The accuracy of radiologists and residents, both with and without AI assistance, was analyzed using a mixed-effects linear model. The accuracies of GPT-4V with different input combinations were compared by using the McNemar test.
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