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Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI Extension

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Title: Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI Extension
Authors: Liu, X
Rivera, SC
Moher, D
Calvert, MJ
Denniston, AK
Ashrafian, H
Beam, AL
Chan, A-W
Collins, GS
Darzi, A
Deeks, JJ
ElZarrad, MK
Espinoza, C
Esteva, A
Faes, L
Di Ruffano, LF
Fletcher, J
Golub, R
Harvey, H
Haug, C
Holmes, C
Jonas, A
Keane, PA
Kelly, CJ
Lee, AY
Lee, CS
Manna, E
Matcham, J
McCradden, M
Monteiro, J
Mulrow, C
Oakden-Rayner, L
Paltoo, D
Panico, MB
Price, G
Rowley, S
Savage, R
Sarkar, R
Vollmer, SJ
Yau, C
Item Type: Journal Article
Abstract: The CONSORT 2010 (Consolidated Standards of Reporting Trials) statement provides minimum guidelines for reporting randomised trials. Its widespread use has been instrumental in ensuring transparency when evaluating new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate impact on health outcomes. The CONSORT-AI extension is a new reporting guideline for clinical trials evaluating interventions with an AI component. It was developed in parallel with its companion statement for clinical trial protocols: SPIRIT-AI. Both guidelines were developed through a staged consensus process, involving a literature review and expert consultation to generate 29 candidate items, which were assessed by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed on in a two-day consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The CONSORT-AI extension includes 14 new items, which were considered sufficiently important for AI interventions, that they should be routinely reported in addition to the core CONSORT 2010 items. CONSORT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention is integrated, the handling of inputs and outputs of the AI intervention, the human-AI interaction and providing analysis of error cases. CONSORT-AI will help promote transparency and completeness in reporting clinical trials for AI interventions. It will assist editors and peer-reviewers, as well as the general readership, to understand, interpret and critically appraise the quality of clinical trial design and risk of bias in the reported outcomes.
Issue Date: 9-Sep-2020
Date of Acceptance: 4-Aug-2020
URI: http://hdl.handle.net/10044/1/85760
DOI: 10.1136/bmj.m3164
ISSN: 0959-535X
Publisher: BMJ Publishing Group
Journal / Book Title: BMJ: British Medical Journal
Volume: 370
Copyright Statement: © 2020 The Author(s). This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/.
Sponsor/Funder: National Institute of Health Research
Keywords: Science & Technology
Life Sciences & Biomedicine
Medicine, General & Internal
General & Internal Medicine
RANDOMIZED-TRIALS
SYSTEM
STATEMENT
QUALITY
PREDICTION
CANCER
Artificial Intelligence
Checklist
Clinical Protocols
Clinical Trials as Topic
Consensus
Delphi Technique
Humans
Research Design
SPIRIT-AI and CONSORT-AI Working Group
Humans
Clinical Protocols
Consensus
Research Design
Artificial Intelligence
Delphi Technique
Clinical Trials as Topic
Checklist
Science & Technology
Life Sciences & Biomedicine
Medicine, General & Internal
General & Internal Medicine
RANDOMIZED-TRIALS
SYSTEM
STATEMENT
QUALITY
PREDICTION
CANCER
General & Internal Medicine
1103 Clinical Sciences
1117 Public Health and Health Services
Publication Status: Published
Open Access location: https://www.bmj.com/content/370/bmj.m3164
Article Number: ARTN m3164
Online Publication Date: 2020-09-09
Appears in Collections:Department of Surgery and Cancer
Faculty of Medicine
Institute of Global Health Innovation



This item is licensed under a Creative Commons License Creative Commons