TY - JOUR AU - Maron, Roman C AU - Utikal, Jochen S AU - Hekler, Achim AU - Hauschild, Axel AU - Sattler, Elke AU - Sondermann, Wiebke AU - Haferkamp, Sebastian AU - Schilling, Bastian AU - Heppt, Markus V AU - Jansen, Philipp AU - Reinholz, Markus AU - Franklin, Cindy AU - Schmitt, Laurenz AU - Hartmann, Daniela AU - Krieghoff-Henning, Eva AU - Schmitt, Max AU - Weichenthal, Michael AU - von Kalle, Christof AU - Fröhling, Stefan AU - Brinker, Titus J PY - 2020 DA - 2020/9/11 TI - Artificial Intelligence and Its Effect on Dermatologists’ Accuracy in Dermoscopic Melanoma Image Classification: Web-Based Survey Study JO - J Med Internet Res SP - e18091 VL - 22 IS - 9 KW - artificial intelligence KW - machine learning KW - deep learning KW - neural network KW - dermatology KW - diagnosis KW - nevi KW - melanoma KW - skin neoplasm AB - Background: Early detection of melanoma can be lifesaving but this remains a challenge. Recent diagnostic studies have revealed the superiority of artificial intelligence (AI) in classifying dermoscopic images of melanoma and nevi, concluding that these algorithms should assist a dermatologist’s diagnoses. Objective: The aim of this study was to investigate whether AI support improves the accuracy and overall diagnostic performance of dermatologists in the dichotomous image–based discrimination between melanoma and nevus. Methods: Twelve board-certified dermatologists were presented disjoint sets of 100 unique dermoscopic images of melanomas and nevi (total of 1200 unique images), and they had to classify the images based on personal experience alone (part I) and with the support of a trained convolutional neural network (CNN, part II). Additionally, dermatologists were asked to rate their confidence in their final decision for each image. Results: While the mean specificity of the dermatologists based on personal experience alone remained almost unchanged (70.6% vs 72.4%; P=.54) with AI support, the mean sensitivity and mean accuracy increased significantly (59.4% vs 74.6%; P=.003 and 65.0% vs 73.6%; P=.002, respectively) with AI support. Out of the 10% (10/94; 95% CI 8.4%-11.8%) of cases where dermatologists were correct and AI was incorrect, dermatologists on average changed to the incorrect answer for 39% (4/10; 95% CI 23.2%-55.6%) of cases. When dermatologists were incorrect and AI was correct (25/94, 27%; 95% CI 24.0%-30.1%), dermatologists changed their answers to the correct answer for 46% (11/25; 95% CI 33.1%-58.4%) of cases. Additionally, the dermatologists’ average confidence in their decisions increased when the CNN confirmed their decision and decreased when the CNN disagreed, even when the dermatologists were correct. Reported values are based on the mean of all participants. Whenever absolute values are shown, the denominator and numerator are approximations as every dermatologist ended up rating a varying number of images due to a quality control step. Conclusions: The findings of our study show that AI support can improve the overall accuracy of the dermatologists in the dichotomous image–based discrimination between melanoma and nevus. This supports the argument for AI-based tools to aid clinicians in skin lesion classification and provides a rationale for studies of such classifiers in real-life settings, wherein clinicians can integrate additional information such as patient age and medical history into their decisions. SN - 1438-8871 UR - https://www.jmir.org/2020/9/e18091 UR - https://doi.org/10.2196/18091 UR - http://www.ncbi.nlm.nih.gov/pubmed/32915161 DO - 10.2196/18091 ID - info:doi/10.2196/18091 ER -