Large Language Models in the Diagnosis of Hand and Peripheral Nerve Injuries: An Evaluation of ChatGPT and the Isabel Differential Diagnosis Generator

Purpose: Tools using artificial intelligence may help reduce missed or delayed diagnoses and improve patient care in hand surgery. This study aimed to compare and evaluate the performance of two natural language processing programs, Isabel and ChatGPT-4, in diagnosing hand and peripheral nerve injuries from a set of clinical vignettes.

Methods: Cases from a virtual library of hand surgery case reports with no history of trauma or previous surgery were included in this study. The clinical details (age, sex, symptoms, signs, and medical history) of 16 hand cases were entered into Isabel and ChatGPT-4 to generate top 10 differential diagnosis lists. Isabel and ChatGPT-4's inclusion and median rank of the correct diagnosis within each list were compared. Two hand surgeons were then provided each list and asked to independently evaluate the performance of the two systems.

Results: Isabel correctly identified 7/16 (44%) cases with a median rank of two (interquartile range = 3). ChatGPT-4 correctly identified 14/16 (88%) of cases with a median rank of one (interquartile range = 1). Physicians one and two, respectively, preferred the lists generated by ChatGPT-4 in 12/16 (75%) and 13/16 (81%) of cases and had no preference in 2/16 (13%) cases.

Conclusions: ChatGPT-4 had significantly greater diagnostic accuracy within our sample (P < .05) and generated higher quality differential diagnoses than Isabel. Isabel produced several inappropriate and imprecise differential diagnoses.

Clinical relevance: Despite large language models' potential utility in generating medical diagnoses, physicians must continue to exercise high caution and use their clinical judgment when making diagnostic decisions.