Comparing Diagnostic Accuracy of ChatGPT to Clinical Diagnosis in General Surgery Consults: A Quantitative Analysis of Disease Diagnosis

Abstract

In summary, this study evaluated the diagnostic accuracy of ChatGPT in identifying three common surgical conditions (acute appendicitis, acute cholecystitis, and diverticulitis) using comprehensive patient data, including age, gender, medical history, medications, symptoms, vital signs, physical exam findings, and basic laboratory results. The hypothesis was that ChatGPT might display slightly lower accuracy rates than clinical diagnoses made by medical providers. The statistical analysis, which included Fisher's exact test, revealed a significant difference between ChatGPT's diagnostic outcomes and clinical diagnoses, particularly in acute cholecystitis and diverticulitis cases. Therefore, we reject the null hypothesis, as the results indicated that ChatGPT's diagnostic accuracy significantly differs from clinical diagnostics in the presented scenarios. However, ChatGPT's overall high accuracy suggests that it can reliably support clinicians, especially in environments where diagnostic resources are limited, and can serve as a valuable tool in military medicine.

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