Breaking barriers: can ChatGPT compete with a shoulder and elbow specialist in diagnosis and management?

Background: ChatGPT is an artificial intelligence (AI) language processing model that uses deep learning to generate human-like responses to natural language inputs. Its potential use in health care has raised questions and several studies have assessed its effectiveness in writing articles, clinical reasoning, and solving complex questions. This study aims to investigate ChatGPT's capabilities and implications in diagnosing and managing patients with new shoulder and elbow complaints in a private clinical setting to provide insights into its potential use as a diagnostic tool for patients and a first consultation resource for primary physicians. Methods: In a private clinical setting, patients were assessed by ChatGPT after being seen by a shoulder and elbow specialist for shoulder and elbow symptoms. To be assessed by the AI model, a research fellow filled out a standardized form (including age, gender, major comorbidities, symptoms and the localization, natural history, and duration, any associated symptoms or movement deficit, aggravating/relieving factors, and x-ray/imaging report if present). This form was submitted through the ChatGPT portal and the AI model was asked for a diagnosis and best management modality. Results: A total of 29 patients with 15 males and 14 females, were included in this study. The AI model was able to correctly choose the diagnosis and management in 93% (27/29) and 83% (24/29) of the patients, respectively. Furthermore, of the remaining 24 patients that were managed correctly, ChatGPT did not specify the appropriate management in 6 patients and chose only one management in 5 patients, where both were applicable and dependent on the patient's choice. Therefore, 55% of ChatGPT's management was poor. Conclusion: ChatGPT made a worthy opponent; however, it will not be able to replace in its current form a shoulder and elbow specialist in diagnosing and treating patients for many reasons such as misdiagnosis, poor management, lack of empathy and interactions with patients, its dependence on magnetic resonance imaging reports, and its lack of new knowledge.