Haibe-Kains, BenjaminAdam, George AlexandruHosny, AhmedKhodakarami, FarnooshWaldron, LeviWang, BoMcIntosh, ChrisGoldenberg, AnnaKundaje, AnshulGreene, Casey S.Broderick, TamaraHoffman, Michael M.Leek, Jeffrey T.Korthauer, KeeganHuber, WolfgangBrazma, AlvisPineau, JoelleTibshirani, RobertHastie, TrevorIoannidis, John P. A.Quackenbush, JohnAerts, Hugo2022-07-262020-10-14Haibe-Kains, Benjamin, George Alexandru Adam, Ahmed Hosny, Farnoosh Khodakarami, Levi Waldron, Bo Wang, Chris McIntosh et al. "Transparency and Reproducibility in Artificial Intelligence." Nature 586, no. 7829 (2020): E14-E16. DOI: 10.1038/s41586-020-2766-y0028-08361476-4687https://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37372879Breakthroughs in artificial intelligence (AI) hold enormous potential as it can automate complex tasks and go even beyond human performance. In their study, McKinney et al. showed the high potential of AI for breast cancer screening. However, the lack of methods’ details and algorithm code undermines its scientific value. Here, we identify obstacles hindering transparent and reproducible AI research as faced by McKinney et al., and provide solutions to these obstacles with implications for the broader field.en-USResearch Subject Categories::MEDICINE::Physiology and pharmacology::Physiology::Medical technologyJournal Article2022-07-2610.1038/s41586-020-2766-y