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A single image-recognition network, trained on about 130,000 clinical photographs, told cancerous skin lesions from benign ones as accurately as 21 dermatologists, the first widely cited demonstration that deep learning could match specialists at a cancer diagnosis task.
Esteva and colleagues at Stanford fine-tuned a convolutional neural network pretrained on everyday images using 129,450 clinical photographs spanning 2,032 skin diseases arranged in a taxonomy. On held-out biopsy-proven images they tested it against 21 board-certified dermatologists on two decisions: keratinocyte carcinomas versus benign seborrheic keratoses, and malignant melanomas versus benign naevi, using both clinical photographs and dermoscopy images. The network's sensitivity and specificity curve matched or exceeded the average dermatologist on each task.
This paper made AI-assisted skin cancer triage a serious clinical prospect and became the template for later work in radiology and pathology. Prospective trials, regulatory clearance and performance across skin tones followed, and are where its promise is now being tested.
Shares Dermoscopy, total-body photography & AI skin analysis, Basal cell carcinoma, Melanoma.
Shares Cutaneous squamous cell carcinoma, Basal cell carcinoma, Melanoma.
Shares Cutaneous squamous cell carcinoma, Basal cell carcinoma.
Shares Cutaneous squamous cell carcinoma, Basal cell carcinoma.
Shares Cutaneous squamous cell carcinoma, Basal cell carcinoma.
Shares Dermoscopy, total-body photography & AI skin analysis, Cutaneous squamous cell carcinoma, Basal cell carcinoma, Melanoma.
Shares Basal cell carcinoma, Melanoma.
Shares Basal cell carcinoma, Melanoma.