TY - JOUR
T1 - Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence
T2 - DECIDE-AI
AU - the 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 - Vasey, Baptiste
AU - Kirby, Barry
AU - Geerts, Bart
AU - Glocker, Ben
AU - Mateen, Bilal A.
AU - Campbell, Bruce
AU - Rogers, Campbell
AU - Velardo, Carmelo
AU - Park, Chang Min
AU - Hehakaya, Charisma
AU - Baber, Chris
AU - Paton, Chris
AU - Fackler, James C.
N1 - 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.
UR - https://www.scopus.com/pages/publications/85130297162
UR - https://www.scopus.com/pages/publications/85130297162#tab=citedBy
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 -