Sarah Haggenmüller, Roman C Maron, Achim Hekler, Jochen S Utikal, Catarina Barata, Raymond L Barnhill, Helmut Beltraminelli, Carola Berking, Brigid Betz-Stablein, Andreas Blum, Stephan A Braun, Richard Carr, Marc Combalia, Maria-Teresa Fernandez-Figueras, Gerardo Ferrara, Sylvie Fraitag, Lars E French, Frank F Gellrich, Kamran Ghoreschi, Matthias Goebeler, Pascale Guitera, Holger A Haenssle, Sebastian Haferkamp, Lucie Heinzerling, Markus V Heppt, Franz J Hilke, Sarah Hobelsberger, Dieter Krahl, Heinz Kutzner, Aimilios Lallas, Konstantinos Liopyris, Mar Llamas-Velasco, Josep Malvehy, Friedegund Meier, Cornelia S L Müller, Alexander A Navarini, Cristián Navarrete-Dechent, Antonio Perasole, Gabriela Poch, Sebastian Podlipnik, Luis Requena, Veronica M Rotemberg, Andrea Saggini, Omar P Sangueza, Carlos Santonja, Dirk Schadendorf, Bastian Schilling, Max Schlaak, Justin G Schlager, Mildred Sergon, Wiebke Sondermann, H Peter Soyer, Hans Starz, Wilhelm Stolz, Esmeralda Vale, Wolfgang Weyers, Alexander Zink, Eva Krieghoff-Henning, Jakob N Kather, Christof von Kalle, Daniel B Lipka, Stefan Fröhling, Axel Hauschild, Harald Kittler, Titus J Brinker
BACKGROUND: Multiple studies have compared the performance of artificial intelligence (AI)-based models for automated skin cancer classification to human experts, thus setting the cornerstone for a successful translation of AI-based tools into clinicopathological practice. OBJECTIVE: The objective of the study was to systematically analyse the current state of research on reader studies involving melanoma and to assess their potential clinical relevance by evaluating three main aspects: test set characteristics (holdout/out-of-distribution data set, composition), test setting (experimental/clinical, inclusion of metadata) and representativeness of participating clinicians...
October 2021: European Journal of Cancer