TY - JOUR
T1 - Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence
T2 - DECIDE-AI
AU - Vasey, Baptiste
AU - Nagendran, Myura
AU - Campbell, Bruce
AU - Clifton, David A.
AU - Collins, Gary S.
AU - Denaxas, Spiros
AU - Denniston, Alastair K.
AU - Faes, Livia
AU - Geerts, Bart
AU - Ibrahim, Mudathir
AU - Liu, Xiaoxuan
AU - Mateen, Bilal A.
AU - Mathur, Piyush
AU - McCradden, Melissa D.
AU - Morgan, Lauren
AU - Ordish, Johan
AU - Rogers, Campbell
AU - Saria, Suchi
AU - Ting, Daniel S. W.
AU - Watkinson, Peter
AU - Weber, Wim
AU - Wheatstone, Peter
AU - McCulloch, Peter
AU - the DECIDE-AI expert group
AU - Lee, Aaron Y.
AU - Fraser, Alan G.
AU - Connell, Ali
AU - Vira, Alykhan
AU - Esteva, Andre
AU - Althouse, Andrew D.
AU - Beam, Andrew L.
AU - de Hond, Anne
AU - Boulesteix, Anne Laure
AU - Bradlow, Anthony
AU - Ercole, Ari
AU - Paez, Arsenio
AU - Tsanas, Athanasios
AU - Kirby, Barry
AU - Glocker, Ben
AU - Velardo, Carmelo
AU - Park, Chang Min
AU - Hehakaya, Charisma
AU - Baber, Chris
AU - Paton, Chris
AU - Johner, Christian
AU - Kelly, Christopher J.
AU - Vincent, Christopher J.
AU - Yau, Christopher
AU - McGenity, Clare
AU - Gatsonis, Constantine
AU - Faivre-Finn, Corinne
AU - Simon, Crispin
AU - Sent, Danielle
AU - Bzdok, Danilo
AU - Treanor, Darren
AU - Wong, David C.
AU - Steiner, David F.
AU - Higgins, David
AU - Benson, Dawn
AU - O’Regan, Declan P.
AU - Gunasekaran, Dinesh V.
AU - Danks, Dominic
AU - Neri, Emanuele
AU - Kyrimi, Evangelia
AU - Schwendicke, Falk
AU - Magrabi, Farah
AU - Ives, Frances
AU - Rademakers, Frank E.
AU - Fowler, George E.
AU - Frau, Giuseppe
AU - Hogg, H. D. Jeffry
AU - Marcus, Hani J.
AU - Chan, Heang-Ping
AU - Xiang, Henry
AU - McIntyre, Hugh F.
AU - Harvey, Hugh
AU - Kim, Hyungjin
AU - Habli, Ibrahim
AU - Fackler, James C.
AU - Shaw, James
AU - Higham, Janet
AU - Wohlgemut, Jared M.
AU - Chong, Jaron
AU - Bibault, Jean-Emmanuel
AU - Cohen, Jérémie F.
AU - Kers, Jesper
AU - Morley, Jessica
AU - Krois, Joachim
AU - Monteiro, Joao
AU - Horovitz, Joel
AU - Fletcher, John
AU - Taylor, Jonathan
AU - Yoon, Jung Hyun
AU - Singh, Karandeep
AU - Moons, Karel G. M.
AU - Karpathakis, Kassandra
AU - Catchpole, Ken
AU - Hood, Kerenza
AU - Balaskas, Konstantinos
AU - Kamnitsas, Konstantinos
AU - Militello, Laura
AU - Wynants, Laure
AU - Oakden-Rayner, Lauren
AU - Lovat, Laurence B.
AU - Smits, Luc J. M.
AU - Hinske, Ludwig C.
AU - ElZarrad, M. Khair
AU - van Smeden, Maarten
AU - Giavina-Bianchi, Mara
AU - Daley, Mark
AU - Sendak, Mark P.
AU - Sujan, Mark
AU - Rovers, Maroeska
AU - DeCamp, Matthew
AU - Woodward, Matthew
AU - Komorowski, Matthieu
AU - Marsden, Max
AU - Mackintosh, Maxine
AU - Abramoff, Michael D.
AU - de la Hoz, Miguel Ángel Armengol
AU - Hambidge, Neale
AU - Daly, Neil
AU - Peek, Niels
AU - Redfern, Oliver
AU - Ahmad, Omer F.
AU - Bossuyt, Patrick M.
AU - Keane, Pearse A.
AU - Ferreira, Pedro N. P.
AU - Schnell-Inderst, Petra
AU - Mascagni, Pietro
AU - Dasgupta, Prokar
AU - Guan, Pujun
AU - Barnett, Rachel
AU - Kader, Rawen
AU - Chopra, Reena
AU - Mann, Ritse M.
AU - Sarkar, Rupa
AU - Mäenpää, Saana M.
AU - Finlayson, Samuel G.
AU - Vollam, Sarah
AU - Vollmer, Sebastian J.
AU - Park, Seong Ho
AU - Laher, Shakir
AU - Joshi, Shalmali
AU - van der Meijden, Siri L.
AU - Shelmerdine, Susan C.
AU - Tan, Tien-En
AU - Stocker, Tom J. W.
AU - Giannini, Valentina
AU - Madai, Vince I.
AU - Newcombe, Virginia
AU - Ng, Wei Yan
AU - Rogers, Wendy A.
AU - Ogallo, William
AU - Park, Yoonyoung
AU - Perkins, Zane B.
N1 - A correction exists for this article and has been included in the published version. It can be found at doi: 0.1038/s41591-022-01951-8
PY - 2022/5/18
Y1 - 2022/5/18
N2 - A growing number of artificial intelligence (AI)-based clinical decision support systems are showing promising performance in preclinical, in silico evaluation, but few have yet demonstrated real benefit to patient care. Early-stage clinical evaluation is important to assess an AI system’s actual clinical performance at small scale, ensure its safety, evaluate the human factors surrounding its use and pave the way to further large-scale trials. However, the reporting of these early studies remains inadequate. The present statement provides a multi-stakeholder, consensus-based reporting guideline for the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI). We conducted a two-round, modified Delphi process to collect and analyze expert opinion on the reporting of early clinical evaluation of AI systems. Experts were recruited from 20 pre-defined stakeholder categories. The final composition and wording of the guideline was determined at a virtual consensus meeting. The checklist and the Explanation & Elaboration (E&E) sections were refined based on feedback from a qualitative evaluation process. In total, 123 experts participated in the first round of Delphi, 138 in the second round, 16 in the consensus meeting and 16 in the qualitative evaluation. The DECIDE-AI reporting guideline comprises 17 AI-specific reporting items (made of 28 subitems) and ten generic reporting items, with an E&E paragraph provided for each. Through consultation and consensus with a range of stakeholders, we developed a guideline comprising key items that should be reported in early-stage clinical studies of AI-based decision support systems in healthcare. By providing an actionable checklist of minimal reporting items, the DECIDE-AI guideline will facilitate the appraisal of these studies and replicability of their findings.
AB - A growing number of artificial intelligence (AI)-based clinical decision support systems are showing promising performance in preclinical, in silico evaluation, but few have yet demonstrated real benefit to patient care. Early-stage clinical evaluation is important to assess an AI system’s actual clinical performance at small scale, ensure its safety, evaluate the human factors surrounding its use and pave the way to further large-scale trials. However, the reporting of these early studies remains inadequate. The present statement provides a multi-stakeholder, consensus-based reporting guideline for the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI). We conducted a two-round, modified Delphi process to collect and analyze expert opinion on the reporting of early clinical evaluation of AI systems. Experts were recruited from 20 pre-defined stakeholder categories. The final composition and wording of the guideline was determined at a virtual consensus meeting. The checklist and the Explanation & Elaboration (E&E) sections were refined based on feedback from a qualitative evaluation process. In total, 123 experts participated in the first round of Delphi, 138 in the second round, 16 in the consensus meeting and 16 in the qualitative evaluation. The DECIDE-AI reporting guideline comprises 17 AI-specific reporting items (made of 28 subitems) and ten generic reporting items, with an E&E paragraph provided for each. Through consultation and consensus with a range of stakeholders, we developed a guideline comprising key items that should be reported in early-stage clinical studies of AI-based decision support systems in healthcare. By providing an actionable checklist of minimal reporting items, the DECIDE-AI guideline will facilitate the appraisal of these studies and replicability of their findings.
UR - http://www.scopus.com/inward/record.url?scp=85136080916&partnerID=8YFLogxK
UR - https://doi.org/10.1038/s41591-022-01951-8
U2 - 10.1038/s41591-022-01772-9
DO - 10.1038/s41591-022-01772-9
M3 - Article
C2 - 35962208
AN - SCOPUS:85136080916
SN - 1078-8956
VL - 28
SP - 924
EP - 933
JO - Nature Medicine
JF - Nature Medicine
ER -