TY - JOUR
T1 - Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence
T2 - DECIDE-AI
AU - DECIDE-AI expert group
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 - Lee, Aaron Y.
AU - Fraser, Alan G.
AU - Denniston, Alastair K.
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 - Vincent, Christopher J.
AU - Yau, Christopher
AU - Faivre-Finn, Corinne
AU - Wong, David C.
AU - Benson, Dawn
AU - Shaw, James
AU - Morley, Jessica
AU - Fletcher, John
AU - Taylor, Jonathan
AU - Balaskas, Konstantinos
AU - Woodward, Matthew
AU - Peek, Niels
AU - McCulloch, Peter
N1 - Funding Information:
The authors would like to thank all Delphi participants and experts who participated in the guideline qualitative evaluation. B.V. would also like to thank B. Beddoe (Sheffield Teaching Hospital), N. Bilbro (Maimonides Medical Center), N. Marlow (Oxford University Hospitals), E. Taylor (Nuffield Department of Surgical Sciences, University of Oxford) and S. Ursprung (Department for Radiology, Tübingen University Hospital) for their support in the initial stage of the project. This work was supported by the IDEAL Collaboration. B.V. is funded by a Berrow Foundation Lord Florey scholarship. M.N. is supported by the UKRI CDT in AI for Healthcare (http://ai4health.io ; grant P/S023283/1). D.C. receives funding from the Wellcome Trust, AstraZeneca, RCUK and GlaxoSmithKline. G.S.C. is supported by the NIHR Biomedical Research Centre, Oxford, and Cancer Research UK (program grant C49297/A27294). M.I. is supported by a Maimonides Medical Center Research fellowship. X.L. receives funding from the Wellcome Trust, the National Institute of Health Research/NHSX/Health Foundation, the Alan Turing Institute, the MHRA and NICE. B.A.M. is a fellow of the Alan Turing Institute, supported by EPSRC grant EP/N510129/, and holds a Wellcome Trust-funded honorary post at University College London for the purposes of carrying out independent research. M.M. receives funding from the Dalla Lana School of Public Health and the Leong Centre for Healthy Children. J.O. is employed by the Medicines and Healthcare products Regulatory Agency, which is the competent authority responsible for regulating medical devices and medicines in the United Kingdom. Elements of the work relating to the regulation of AI as a medical device are funded by grants from NHSX and the Regulators’ Pioneer Fund (Department for Business, Energy and Industrial Strategy). S.S. receives grants from the National Science Foundation, the American Heart Association, the National Institutes of Health and the Sloan Foundation. D.S.W.T. is supported by the National Medical Research Council, Singapore (NMRC/HSRG/0087/2018;MOH-000655-00), the National Health Innovation Centre, Singapore (NHIC-COV19-2005017), the SingHealth Fund Limited Foundation (SHF/HSR113/2017), the Duke-NUS Medical School (Duke-NUS/RSF/2021/0018;05/FY2020/EX/15-A58) and the Agency for Science, Technology and Research (A20H4g2141; H20C6a0032). P. Watkinson is supported by the NIHR Biomedical Research Centre, Oxford, and holds grants from the NIHR and Wellcome. P. McCulloch receives grants from Medtronic (unrestricted educational grant to Oxford University for the IDEAL Collaboration) and the Oxford Biomedical Research Centre. The views expressed in this guideline are those of the authors, Delphi participants and experts who participated in the qualitative evaluation of the guideline. These views do not necessarily reflect those of their institutions or funders.
Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Nature America, Inc.
PY - 2022/5
Y1 - 2022/5
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.
KW - Artificial Intelligence
KW - Checklist
KW - Consensus
KW - Humans
KW - Research Design
KW - Research Report
UR - http://www.scopus.com/inward/record.url?scp=85130297162&partnerID=8YFLogxK
U2 - 10.1038/s41591-022-01772-9
DO - 10.1038/s41591-022-01772-9
M3 - Review article
C2 - 35585198
AN - SCOPUS:85130297162
SN - 1078-8956
VL - 28
SP - 924
EP - 933
JO - Nature Medicine
JF - Nature Medicine
IS - 5
ER -