Diagnosis of Skin Disease in Moderately to Highly Pigmented Skin by Artificial Intelligence.

Background: Triage of patients with skin diseases often includes an initial assessment by a nurse or general practitioner, followed by a dermatologist. Artificial intelligence (AI) systems have been reported to improve clinician ability to diagnose and triage skin conditions. Previous studies have also shown that diagnosis in patients with skin of color can be more challenging.

Purpose: This study seeks to determine the performance of AI in the screening and triage of benign-neoplastic, malignant-neoplastic, and non-neoplastic skin conditions for Fitzpatrick skin types IV-VI.

Methods: A set of 163 non-standardized clinical photographs of skin disease manifestations from patients with Fitzpatrick skin types IV-VI were obtained through a publicly available dataset (Scale AI and MIT Research Lab, “Fitzpatrick 17 Dataset”). All photos were diagnosed by a specialist and categorized into three disease classes: benign-neoplastic, malignant-neoplastic, or non-neoplastic. There were 23, 14, and 122 cases of each disease class, respectively.

Results: Overall, the AI was able to classify the disease classes with a high degree of accuracy for the Top 1 diagnosis (86.50%). Based on its first prediction, the AI demonstrated the greatest accuracy when classifying non-neoplastic conditions (90.98%), high accuracy in detecting malignant-neoplastic conditions (77.78%), and moderate accuracy of classifying benign-neoplastic conditions (69.57%).

Conclusion: The AI had an overall accuracy of 86.50% in diagnosing skin disease in Fitzpatrick skin types IV to VI. This is an improvement over reported clinician diagnostic accuracy of 44.3% in darker skin types. Incorporating AI into front-line screening of skin conditions could thereby assist in patient triage and shorten the time to accurate diagnosis. Schneider LG, Mamelak AJ, Tejani I, et al. Diagnosis of skin disease in moderately to highly pigmented skin by artificial intelligence. J Drugs Dermatol. 2023;22(7):647-652. doi:10.36849/JDD.7581.