Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI
Vasey, Baptiste Nagendran, Myura Campbell, Bruce Clifton, David A Collins, Gary S Denaxas, Spiros Denniston, Alastair K Faes, Livia Geerts, Bart Ibrahim, Mudathir
Vasey, Baptiste
Nagendran, Myura
Campbell, Bruce
Clifton, David A
Collins, Gary S
Denaxas, Spiros
Denniston, Alastair K
Faes, Livia
Geerts, Bart
Ibrahim, Mudathir
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Affiliation
University of Oxford; Imperial College London; University of Exeter Medical School; Royal Devon and Exeter Hospital; University College London; British Heart Foundation Data Science Centre; Health Data Research UK; UCL Hospitals Biomedical Research Centre; University Hospitals Birmingham NHS Foundation Trust; University of Birmingham; Moorfields Eye Hospital NHS Foundation Trust; Healthplus.ai-R&D; Maimonides Medical Center; Wellcome Trust; Alan Turing Institute; Cleveland Clinic; Hospital for Sick Children; University of Toronto; Morgan Human Systems; The Medicines and Healthcare products Regulatory Agency; HeartFlow; Johns Hopkins University; Bayesian Health; Singapore Eye Research Institute; National University of Singapore; Oxford University Hospitals NHS Trust; The BMJ; University of Leeds
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Publication date
2022-05-18
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Abstract
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 multistakeholder, 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 analyse expert opinion on the reporting of early clinical evaluation of AI systems. Experts were recruited from 20 predefined 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. 123 experts participated in the first round of Delphi, 138 in the second, 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 10 generic reporting items, with an E&E paragraph provided for each. Through consultation and consensus with a range of stakeholders, we have 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.
Citation
Vasey B, Nagendran M, Campbell B, Clifton DA, Collins GS, Denaxas S, Denniston AK, Faes L, Geerts B, Ibrahim M, Liu X, Mateen BA, Mathur P, McCradden MD, Morgan L, Ordish J, Rogers C, Saria S, Ting DSW, Watkinson P, Weber W, Wheatstone P, McCulloch P; DECIDE-AI expert group. Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. BMJ. 2022 May 18;377:e070904. doi: 10.1136/bmj-2022-070904.
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Article