Validity of machine learning in biology and medicine increased through collaborations across fields of expertise

• Published in: Nature Machine Intelligence Vol. 2; no. 1; pp. 18 - 24
• Main Authors: Littmann, Maria, Selig, Katharina, Cohen-Lavi, Liel, Frank, Yotam, Hönigschmid, Peter, Kataka, Evans, Mösch, Anja, Qian, Kun, Ron, Avihai, Schmid, Sebastian, Sorbie, Adam, Szlak, Liran, Dagan-Wiener, Ayana, Ben-Tal, Nir, Niv, Masha Y., Razansky, Daniel, Schuller, Björn W., Ankerst, Donna, Hertz, Tomer, Rost, Burkhard
• Format: Journal Article
• Language: English
• Published: London Springer Science and Business Media LLC 01.01.2020; Nature Publishing Group UK; Nature Publishing Group
• Subjects: 10050 Institute of Pharmacology and Toxicology; 10237 Institute of Biomedical Engineering; 170 Ethics; 610 Medicine & health; 631/114/1305; 631/114/2399; 631/114/2415; 631/1647/794; Artificial intelligence; Automation; Bioinformatics; Biology; Datasets; Engineering; Impact factors; Interdisciplinary aspects; Life sciences; Machine learning; Medicine; Perspective; Scientific papers; Scientific validity; Scientists; Validity

Machine learning (ML) has become an essential asset for the life sciences and medicine. We selected 250 articles describing ML applications from 17 journals sampling 26 different fields between 2011 and 2016. Independent evaluation by two readers highlighted three results. First, only half of the ar...