Evaluation of a Large Language Model’s Ability to Assist in an Orthopedic Hand Clinic

Abstract

Background: Advancements in artificial intelligence technology, such as OpenAI’s large language model, ChatGPT, could transform medicine through applications in a clinical setting. This study aimed to assess the utility of ChatGPT as a clinical assistant in an orthopedic hand clinic. Methods: Nine clinical vignettes, describing various common and uncommon hand pathologies, were constructed and reviewed by 4 fellowship-trained orthopedic hand surgeons and an orthopedic resident. ChatGPT was given these vignettes and asked to generate a differential diagnosis, potential workup plan, and provide treatment options for its top differential. Responses were graded for accuracy and the overall utility scored on a 5-point Likert scale. Results: The diagnostic accuracy of ChatGPT was 7 out of 9 cases, indicating an overall accuracy rate of 78%. ChatGPT was less reliable with more complex pathologies and failed to identify an intentionally incorrect presentation. ChatGPT received a score of 3.8 ± 1.4 for correct diagnosis, 3.4 ± 1.4 for helpfulness in guiding patient management, 4.1 ± 1.0 for appropriate workup for the actual diagnosis, 4.3 ± 0.8 for an appropriate recommended treatment plan for the diagnosis, and 4.4 ± 0.8 for the helpfulness of treatment options in managing patients. Conclusion: ChatGPT was successful in diagnosing most of the conditions; however, the overall utility of its advice was variable. While it performed well in recommending treatments, it faced difficulties in providing appropriate diagnoses for uncommon pathologies. In addition, it failed to identify an obvious error in presenting pathology.