We conducted a prospective, observational clinical validation study to assess the diagnostic accuracy of the AI algorithm (ADAE) in predicting melanoma from dermoscopy skin lesion images. Patients who had consented for a skin biopsy to exclude melanoma were eligible. All lesions underwent biopsy.

Dataset from a prospective, observational clinical cohort study to assess the consistency of two commercially available convolutional neural networks (CNNs) in classifying melanoma risk of five sequentially acquired dermoscopic images of melanocytic lesions on the torso. 117 repeat image series of 116 melanocytic lesions from 66 patients were included. Biopsies were performed in cases of suspected melanoma or two consecutive elevated CNN risk scores. Expert consensus including 1-year follow-up images (where available) confirmed benign dignity of lesions without histological assessment. Goessinger EV, Cerminara SE, Mueller AM, et al. Consistency of convolutional neural networks in dermoscopic melanoma recognition: A prospective real-world study about the pitfalls of augmented intelligence. J Eur Acad Dermatol Venereol. 2024;38(5):945-953. doi:10.1111/jdv.19777

Moles and melanomas. Biopsy-confirmed melanocytic lesions. Both malignant and benign lesions are included.