Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

被引:0
作者
Baptiste Vasey
Myura Nagendran
Bruce Campbell
David A. Clifton
Gary S. Collins
Spiros Denaxas
Alastair K. Denniston
Livia Faes
Bart Geerts
Mudathir Ibrahim
Xiaoxuan Liu
Bilal A. Mateen
Piyush Mathur
Melissa D. McCradden
Lauren Morgan
Johan Ordish
Campbell Rogers
Suchi Saria
Daniel S. W. Ting
Peter Watkinson
Wim Weber
Peter Wheatstone
Peter McCulloch
机构
[1] University of Oxford,Nuffield Department of Surgical Sciences
[2] University of Oxford,Institute of Biomedical Engineering, Department of Engineering Science
[3] University of Oxford,Critical Care Research Group, Nuffield Department of Clinical Neurosciences
[4] Imperial College London,UKRI Centre for Doctoral Training in AI for Healthcare
[5] University of Exeter Medical School,Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology & Musculoskeletal Sciences
[6] Royal Devon and Exeter Hospital,Institute of Health Informatics
[7] University of Oxford,Department of Surgery
[8] University College London,Department of General Anesthesiology
[9] British Heart Foundation Data Science Centre,Departments of Computer Science, Statistics, and Health Policy, and Division of Informatics
[10] Health Data Research UK,Singapore National Eye Center
[11] UCL Hospitals Biomedical Research Centre,Department of Ophthalmology, School of Medicine
[12] University Hospitals Birmingham NHS Foundation Trust,School of Medicine
[13] Academic Unit of Ophthalmology,Department of Epidemiology
[14] Institute of Inflammation and Ageing,CAIRElab
[15] College of Medical and Dental Sciences,Institute for Biomedical Information Processing, Biometry and Epidemiology
[16] University of Birmingham,Rheumatology Department
[17] Moorfields Eye Hospital NHS Foundation Trust,Cambridge Centre for AI in Medicine
[18] Healthplus.ai-R&D BV,Nuffield Department of Primary Care Health Sciences
[19] Maimonides Medical Center,Usher Institute, Edinburgh Medical School
[20] The Wellcome Trust,Department of Computing
[21] The Alan Turing Institute,Division of Imaging & Oncology
[22] Anesthesiology Institute,School of Computer Science
[23] Cleveland Clinic,Nuffield Department of Medicine
[24] The Hospital for Sick Children,Pathology and Data Analytics
[25] Dalla Lana School of Public Health,Department of Biostatistics
[26] University of Toronto,Department of Medical Informatics
[27] Morgan Human Systems Ltd,Department of Computer Science and Centre for Health Informatics
[28] Medicines and Healthcare products Regulatory Agency,MRC London Institute of Medical Sciences
[29] HeartFlow Inc.,Institute of Cancer and Genomic Sciences
[30] Johns Hopkins University,School of Electronic Engineering and Computer Science
[31] Bayesian Health,Australian Institute of Health Innovation
[32] Singapore Eye Research Institute,Department of Cardiovascular Sciences
[33] Duke-NUS Medical School,Bristol Centre for Surgical Research, Department of Population Health Sciences
[34] National University of Singapore,Population Health Science Institute
[35] NIHR Biomedical Research Centre Oxford,Department of Neurosurgery
[36] Oxford University Hospitals NHS Trust,Department of Radiology
[37] The BMJ,The Abigail Wexner Research Institute, Nationwide Children’s Hospital
[38] School of Medicine,Department of Medicine
[39] University of Leeds,Department of Radiology
[40] University of Washington,Department of Computer Science
[41] Cardiff University,Department of Anesthesiology and Critical Care Medicine
[42] Google Health,Joint Centre for Bioethics
[43] Quantium Health,Centre for Trauma Sciences, Blizard Institute
[44] Artera Research,Department of Medical Imaging
[45] Artera,Radiation Oncology Department
[46] University of Pittsburgh,Center of Research in Epidemiology and Statistics (Inserm 1153)
[47] Harvard T.H. Chan School of Public Health,Department of Pathology, Amsterdam University Medical Center
[48] Leiden University Medical Centre,Oxford Internet Institute
[49] Ludwig-Maximilians-University Munich,Oral Diagnostics & Digital Health & Health Services Research
[50] Royal Berkshire Hospital,Nuclear Medicine / 3DLab
来源
Nature Medicine | 2022年 / 28卷
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摘要
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.
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页码:924 / 933
页数:9
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