RT Journal Article
T1 Transparency and reproducibility in artificial intelligence.
A1 Haibe-Kains, Benjamin
A1 Adam, George Alexandru
A1 Hosny, Ahmed
A1 Khodakarami, Farnoosh
A1 Massive Analysis Quality Control (MAQC) Society Board of Directors, 
A1 Waldron, Levi
A1 Wang, Bo
A1 McIntosh, Chris
A1 Goldenberg, Anna
A1 Kundaje, Anshul
A1 Greene, Casey S
A1 Broderick, Tamara
A1 Hoffman, Michael M
A1 Leek, Jeffrey T
A1 Korthauer, Keegan
A1 Huber, Wolfgang
A1 Brazma, Alvis
A1 Pineau, Joelle
A1 Tibshirani, Robert
A1 Hastie, Trevor
A1 Ioannidis, John P A
A1 Quackenbush, John
A1 Aerts, Hugo J W L
AB Breakthroughs 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.
YR 2020
FD 2020-10-14
LK http://hdl.handle.net/10668/16422
UL http://hdl.handle.net/10668/16422
LA en
DS RISalud
RD Apr 8, 2025