Multiclass convolutional neural network vs . 96 dermatologists in skin lesion diagnosis, an international study

Summary Background and objectives Artificial intelligence was shown to improve diagnostic accuracy for skin cancer detection. While most clinically approved models provide binary “benign/malignant” classifications, multiclass predictions may offer greater clinical utility. Yet, comparisons between multiclass convolutional neural networks (CNNs) and dermatologists are scarce. Methods In an international web‐based reader study, dermatologists (n = 96) and a prototype multiclass CNN (FotoFinder Systems, Germany) assigned diagnoses to 100 skin lesions by using nine disease categories (melanoma; basal cell carcinoma; squamous cell carcinoma; intraepithelial carcinoma; melanocytic nevus; benign keratinocytic lesion; dermatofibroma; vascular lesion; “other”). The main outcome measure was overall mean sensitivity (micro‐averaged percentage of correct diagnosis) of dermatologists versus multiclass CNN. Results Dermatologists achieved an overall mean sensitivity (95% CI) of 69.4% (68.4%–70.3%) with dermatoscopy‐only data (level‐I), which improved to 76.0% (75.2%–76.9%) when provided with full clinical information (level‐II). The CNN's top‐rank predictions showed a higher overall mean sensitivity of 82.0% (73.3%–88.3%). The CNN significantly outperformed all dermatologist subgroups, except for dermatoscopy experts at study level‐II (overall mean sensitivity 81.1% [79.9%–82.3%], p equivalence = 0.0085). Conclusions Multiclass CNN predictions outperformed most dermatologists in diagnostic accuracy, supporting their potential to enhance clinical decision‐making, particularly in settings with limited dermatological expertise.