A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: QUADAS-AI

Viknesh Sounderajah*, Hutan Ashrafian, Sherri Rose, Nigam H. Shah, Marzyeh Ghassemi, Robert Golub, Charles E. Kahn, Andre Esteva, Alan Karthikesalingam, Bilal Mateen, Dale Webster, Dan Milea, Daniel Ting, Darren Treanor, Dominic Cushnan, Dominic King, Duncan McPherson, Ben Glocker, Felix Greaves, Leanne HarlingJohan Ordish, Jérémie F. Cohen, Jon Deeks, Mariska Leeflang, Matthew Diamond, Matthew D.F. McInnes, Melissa McCradden, Michael D. Abràmoff, Pasha Normahani, Sheraz R. Markar, Stephanie Chang, Xiaoxuan Liu, Susan Mallett, Shravya Shetty, Alastair Denniston, Gary S. Collins, David Moher, Penny Whiting, Patrick M. Bossuyt, Ara Darzi

*Corresponding author for this work

Research output: Contribution to journalLetterpeer-review

100 Scopus citations
Original languageEnglish (US)
Pages (from-to)1663-1665
Number of pages3
JournalNature Medicine
Volume27
Issue number10
DOIs
StatePublished - Oct 2021

Funding

Infrastructure support for this research was provided by the National Institute for Health Research (NIHR) Imperial Biomedical Research Centre (BRC). G.S.C. is supported by the NIHR Biomedical Research Centre and Cancer Research UK (programme grant C49297/A27294). D.T. is funded by National Pathology Imaging Co-operative, NPIC (project no. 104687), supported by a \u00A350 million investment from the Data to Early Diagnosis and Precision Medicine strand of the government\u2019s Industrial Strategy Challenge Fund, and managed and delivered by UK Research and Innovation (UKRI). F.G. is supported by the NIHR Applied Research Collaboration Northwest London. The views and opinions expressed herein are those of the authors and do not necessarily reflect the views of their employers or funders.

ASJC Scopus subject areas

  • General Biochemistry, Genetics and Molecular Biology

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