Improving patient prostate cancer risk assessment: Moving from static, globally-applied to dynamic, practice-specific risk calculators. - Andreas N. Strobl; Andrew J. Vickers; Ben Van Calster; Ewout W. Steyerberg; Robin J. Leach; Ian M. Thompson; Donna P. Ankerst (J. Biomed. Informatics, 2015)

Three general concepts to improve risk prediction: good data, wisdom of the crowd, recalibration. - Ivan Kondofersky; Michael Laimighofer; Christoph Kurz; Norbert Krautenbacher; Julia F. Söllner; Philip Dargatz; Hagen Scherb; Donna P. Ankerst; Christiane Fuchs (F1000Research, 2016)

Validity of machine learning in biology and medicine increased through collaborations across fields of expertise. - Maria Littmann; Katharina Selig; Liel Cohen-Lavi; Yotam Frank; Peter Hönigschmid; Evans Kataka; Anja Mösch; Kun Qian 003; Avihai Ron; Sebastian Schmid 002; Adam Sorbie; Liran Szlak; et al. (Nat. Mach. Intell., 2020)

Are we using appropriate segmentation metrics? Identifying correlates of human expert perception for CNN training beyond rolling the DICE coefficient. - Florian Kofler; Ivan Ezhov; Fabian Isensee; Fabian Balsiger; Christoph Berger; Maximilian Koerner; Johannes C. Paetzold; Hongwei Li 004; Suprosanna Shit; Richard McKinley; Spyridon Bakas; Claus Zimmer; et al. (CoRR, 2021)