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Transparency and reproducibility in artificial intelligence

2020; Nature Portfolio; Volume: 586; Issue: 7829 Linguagem: Inglês

10.1038/s41586-020-2766-y

1476-4687

Benjamin Haibe‐Kains, George Alexandru Adam, Ahmed Hosny, Farnoosh Khodakarami, Thakkar Shraddha, Rebecca Kusko, Susanna‐Assunta Sansone, Weida Tong, Russ Wolfinger, Christopher E. Mason, Wendell Jones, Joaquı́n Dopazo, Cesare Furlanello, Levi Waldron, Bo Wang, Chris McIntosh, Anna Goldenberg, Anshul Kundaje, Casey S. Greene, Tamara Broderick, Michael M. Hoffman, Jeffrey T. Leek, Keegan Korthauer, Wolfgang Huber, Alvis Brāzma, Joëlle Pineau, Robert Tibshirani, Trevor Hastie, John P. A. Ioannidis, John Quackenbush, Hugo J.W.L. Aerts,

Radiomics and Machine Learning in Medical Imaging

In their study, McKinney et al. showed the high potential of artificial intelligence for breast cancer screening. However, the lack of detailed methods and computer code undermines its scientific value. We identify obstacles hindering transparent and reproducible AI research as faced by McKinney et al and provide solutions with implications for the broader field.