Assessing Performance of Multimodal ChatGPT-4 on an image based Radiology Board-style Examination: An exploratory study

Abstract

ABSTRACT Objective To evaluate the performance of multimodal ChatGPT 4 on a radiology board-style examination containing text and radiologic images. s Materials and Methods In this prospective exploratory study from October 30 to December 10, 2023, 110 multiple-choice questions containing images designed to match the style and content of radiology board examination like the American Board of Radiology Core or Canadian Board of Radiology examination were prompted to multimodal ChatGPT 4. Questions were further sub stratified according to lower-order (recall, understanding) and higher-order (analyze, synthesize), domains (according to radiology subspecialty), imaging modalities and difficulty (rated by both radiologists and radiologists-in-training). ChatGPT performance was assessed overall as well as in subcategories using Fisher’s exact test with multiple comparisons. Confidence in answering questions was assessed using a Likert scale (1-5) by consensus between a radiologist and radiologist-in-training. Reproducibility was assessed by comparing two different runs using two different accounts. Results ChatGPT 4 answered 55% (61/110) of image-rich questions correctly. While there was no significant difference in performance amongst the various sub-groups on exploratory analysis, performance was better on lower-order [61% (25/41)] when compared to higher-order [52% (36/69)] [P=.46]. Among clinical domains, performance was best on cardiovascular imaging [80% (8/10)], and worst on thoracic imaging [30% [3/10)]. Confidence in answering questions was confident/highly confident [89%(98/110)], even when incorrect There was poor reproducibility between two runs, with the answers being different in 14% (15/110) questions. Conclusion Despite no radiology specific pre-training, multimodal capabilities of ChatGPT appear promising on questions containing images. However, the lack of reproducibility among two runs, even with the same questions poses challenges of reliability.

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