A Point-of-Care, Real-Time Artificial Intelligence System to Support Clinician Diagnosis of a Wide Range of Skin Diseases

Abstract

Dermatological diagnosis remains challenging for nonspecialists because the morphologies of primary skin lesions widely vary from patient to patient. Although previous studies have used artificial intelligence (AI) to classify lesions as benign or malignant, there have not been extensive studies examining the use of AI on identifying and categorizing a primary skin lesion's morphology. In this study, we evaluate the performance of a standalone AI tool to correctly categorize a skin lesion's morphology from a test bank of images. To provide a marker of performance, we evaluate the accuracy of primary care physicians to categorize skin lesion morphology in the same test bank of images without any aids and then with the aid of a simple visual guide. The AI system achieved an accuracy of 68% in determining the single most likely morphology from the test image bank. When the AI's top prediction was broadened to its top three most likely predictions, accuracy improved to 80%. In comparison, the diagnostic accuracy of primary care physicians was 36% without any aids and 68% with the visual guide (P < 0.001). The AI was subsequently tested on an additional set of 222 heterogeneous images of varying Fitzpatrick skin types and achieved an overall accuracy of 70% in the Fitzpatrick I-III skin type group and 68% in the Fitzpatrick IV-VI skin type group (P= 0.79). An AI is a powerful tool to assist physicians in the diagnosis of skin lesions while still requiring the user to critically consider other possible diagnoses.