A Comparison of ChatGPT-4 Vision’s Diagnostic Accuracy for Inpatient Skin Conditions in White and Skin of Color Patients
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Keywords
Artificial Intelligence, ChatGPT, ChatGPT-4V, clinical images, inpatient dermatology, skin of color
Abstract
The objective of this study was to assess ChatGPT-4 Vision's (ChatGPT-4V) ability to diagnose inpatient skin conditions in white and skin of color (SOC) individuals based solely on images. To do this, five white and five SOC images depicting the fifteen most common inpatient dermatologic conditions in the United States were selected from VisualDx, dermatology textbooks, and dermatology journals. ChatGPT-4V was graded on its ability to correctly diagnose the dermatologic condition or to list the correct diagnosis in its top three differential diagnoses. ChatGPT-4V correctly diagnosed a greater percentage of images depicting white individuals (43/75, 57.3%) than images depicting SOC individuals (32/75, 42.7%), though this difference was not statistically significant (P=0.103). While the accuracy increased when assessing ChatGPT-4V's top differential diagnoses, it did not eclipse 75% for white or SOC images. Accuracy varied depending on the condition that was presented. The findings of this study suggest ChatGPT-4V cannot be used as a reliable diagnostic tool to diagnose inpatient skin conditions in white or SOC individuals.
References
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Jesse Hirner, University of Nebraska Medical Center Department of Dermatology
Assistant Professor in the University of Nebraska Medical Center Department of Dermatology
Erin X Wei, University of Nebraska Medical Center Department of Dermatology
Associate Professor in the University of Nebraska Medical Center Department of Dermatology