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 Harling; Johan Ordish; J. rémie 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

doi:https://doi.org/10.1038/s41591-021-01517-0

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, J. rémie 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

Research output: Contribution to journal › Comment/Letter to the editor › Academic

61 Citations (Scopus)

Original language	English
Pages (from-to)	1663-1665
Number of pages	3
Journal	Nature medicine
Volume	27
Issue number	10
DOIs	https://doi.org/10.1038/s41591-021-01517-0
Publication status	Published - 1 Oct 2021

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Sounderajah, V., Ashrafian, H., Rose, S., Shah, N. H., Ghassemi, M., Golub, R., Kahn, C. E., Esteva, A., Karthikesalingam, A., Mateen, B., Webster, D., Milea, D., Ting, D., Treanor, D., Cushnan, D., King, D., McPherson, D., Glocker, B., Greaves, F., ... Darzi, A. (2021). A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: QUADAS-AI. Nature medicine, 27(10), 1663-1665. https://doi.org/10.1038/s41591-021-01517-0

@article{0a3b414cde3745c29bb207275b2ce422,

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

author = "Viknesh Sounderajah and Hutan Ashrafian and Sherri Rose and Shah, {Nigam H.} and Marzyeh Ghassemi and Robert Golub and Kahn, {Charles E.} and Andre Esteva and Alan Karthikesalingam and Bilal Mateen and Dale Webster and Dan Milea and Daniel Ting and Darren Treanor and Dominic Cushnan and Dominic King and Duncan McPherson and Ben Glocker and Felix Greaves and Leanne Harling and Johan Ordish and Cohen, {J. r{\'e}mie F.} and Jon Deeks and Mariska Leeflang and Matthew Diamond and McInnes, {Matthew D. F.} and Melissa McCradden and Abr{\`a}moff, {Michael D.} and Pasha Normahani and Markar, {Sheraz R.} and Stephanie Chang and Xiaoxuan Liu and Susan Mallett and Shravya Shetty and Alastair Denniston and Collins, {Gary S.} and David Moher and Penny Whiting and Bossuyt, {Patrick M.} and Ara Darzi",

note = "Funding Information: 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 £50 million investment from the Data to Early Diagnosis and Precision Medicine strand of the government{\textquoteright}s 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.",

year = "2021",

month = oct,

day = "1",

doi = "https://doi.org/10.1038/s41591-021-01517-0",

language = "English",

volume = "27",

pages = "1663--1665",

journal = "Nature medicine",

issn = "1078-8956",

publisher = "Nature Publishing Group",

number = "10",

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Sounderajah, V, Ashrafian, H, Rose, S, Shah, NH, Ghassemi, M, Golub, R, Kahn, CE, Esteva, A, Karthikesalingam, A, Mateen, B, Webster, D, Milea, D, Ting, D, Treanor, D, Cushnan, D, King, D, McPherson, D, Glocker, B, Greaves, F, Harling, L, Ordish, J, Cohen, JRF, Deeks, J, Leeflang, M, Diamond, M, McInnes, MDF, McCradden, M, Abràmoff, MD, Normahani, P, Markar, SR, Chang, S, Liu, X, Mallett, S, Shetty, S, Denniston, A, Collins, GS, Moher, D, Whiting, P, Bossuyt, PM & Darzi, A 2021, 'A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: QUADAS-AI', Nature medicine, vol. 27, no. 10, pp. 1663-1665. https://doi.org/10.1038/s41591-021-01517-0

TY - JOUR

T1 - A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies

T2 - QUADAS-AI

AU - Sounderajah, Viknesh

AU - Ashrafian, Hutan

AU - Rose, Sherri

AU - Shah, Nigam H.

AU - Ghassemi, Marzyeh

AU - Golub, Robert

AU - Kahn, Charles E.

AU - Esteva, Andre

AU - Karthikesalingam, Alan

AU - Mateen, Bilal

AU - Webster, Dale

AU - Milea, Dan

AU - Ting, Daniel

AU - Treanor, Darren

AU - Cushnan, Dominic

AU - King, Dominic

AU - McPherson, Duncan

AU - Glocker, Ben

AU - Greaves, Felix

AU - Harling, Leanne

AU - Ordish, Johan

AU - Cohen, J. rémie F.

AU - Deeks, Jon

AU - Leeflang, Mariska

AU - Diamond, Matthew

AU - McInnes, Matthew D. F.

AU - McCradden, Melissa

AU - Abràmoff, Michael D.

AU - Normahani, Pasha

AU - Markar, Sheraz R.

AU - Chang, Stephanie

AU - Liu, Xiaoxuan

AU - Mallett, Susan

AU - Shetty, Shravya

AU - Denniston, Alastair

AU - Collins, Gary S.

AU - Moher, David

AU - Whiting, Penny

AU - Bossuyt, Patrick M.

AU - Darzi, Ara

N1 - Funding Information: 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 £50 million investment from the Data to Early Diagnosis and Precision Medicine strand of the government’s 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.

PY - 2021/10/1

Y1 - 2021/10/1

UR - http://www.scopus.com/inward/record.url?scp=85116809646&partnerID=8YFLogxK

U2 - https://doi.org/10.1038/s41591-021-01517-0

DO - https://doi.org/10.1038/s41591-021-01517-0

M3 - Comment/Letter to the editor

C2 - 34635854

SN - 1078-8956

VL - 27

SP - 1663

EP - 1665

JO - Nature medicine

JF - Nature medicine

IS - 10

ER -

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

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