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

SPIRIT-AI and CONSORT-AI Working Group

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The CONSORT 2010 statement provides minimum guidelines for reporting randomised trials. Its widespread use has been instrumental in ensuring transparency in the evaluation of 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 (Consolidated Standards of Reporting Trials-Artificial Intelligence) 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 (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving 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 upon in a two-day consensus meeting (31 stakeholders), and refined through a checklist pilot (34 participants). The CONSORT-AI extension includes 14 new items that 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 provision of an 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.
Original languageEnglish
Pages (from-to)E537-E548
Number of pages12
JournalThe Lancet Digital Health
Issue number10
Early online date9 Sept 2020
Publication statusPublished - Oct 2020

Bibliographical note

Funding Information:
MJC has received personal fees from Astellas, Takeda, Merck, Daiichi Sankyo, Glaukos, GlaxoSmithKline, and the Patient-Centered Outcomes Research Institute (PCORI), outside the submitted work. ADa is an advisor for Google DeepMind, outside the submitted work. LF reports personal fees from Allergan, Bayer, and Novartis, outside the submitted work. JF reports personal fees from British Medical Journal, during the conduct of the study. HH reports that he is Managing Director at Hardian Health, consultancy for health technology firms. PAK reports personal fees from DeepMind Technologies, Roche, Novartis, Apellis, Bayer, Allergan, Topcon, and Heidelberg Engineering, outside the submitted work. AYL reports personal fees from Genentech, US Food and Drug Administration, and Verana Health, grants from Microsoft, NVIDIA, Carl Zeiss Meditec, and Santen, outside the submitted work. CSL reports grants from National Institute of Health/ National Institute on Aging, outside the submitted work. CJK is an employee of Google and owns Alphabet stock. AE is an employee of Salesforce CRM. RiS is an employee of Pinpoint Science. JMa was an employee of AstraZeneca PLC at the time of this study. RuS is Editor-in-Chief of The Lancet Digital Health and reports personal fees from The Lancet Group, during the conduct of the study. JMo is Chief Editor of the journal Nature Medicine ; he has recused himself from any aspect of decision-making on this manuscript and played no part in the assignment of this manuscript to in-house editors or peer reviewers, and was also separated and blinded from the editorial process from submission inception to decision. SJV reports funding from IQVIA. All other authors declare no competing interests.

Funding Information:
We thank the participants who were involved in the Delphi study and Pilot study (Supplementary Note), Eliot Marston for providing strategic support (University of Birmingham, Birmingham, UK), and Charlotte Radovanovic (University Hospitals Birmingham NHS Foundation Trust, UK) and Anita Walker (University of Birmingham, UK) for administrative support. The views expressed in this publication are those of the authors, Delphi participants and stakeholder participants and may not represent the views of the broader stakeholder group or host institution. This work was funded by a Wellcome Trust Institutional Strategic Support Fund: Digital Health Pilot Grant, Research England (part of UK Research and Innovation), Health Data Research UK, and the Alan Turing Institute. The study was sponsored by the University of Birmingham, UK. The study funders and sponsors had no role in the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review or approval of the manuscript; or decision to submit the manuscript for publication. MJC is a National Institute for Health Research (NIHR) Senior Investigator and receives funding from the NIHR Birmingham Biomedical Research Centre; the NIHR Surgical Reconstruction and Microbiology Research Centre and NIHR ARC West Midlands at the University of Birmingham and University Hospitals Birmingham NHS Foundation Trust; Health Data Research UK; Innovate UK (part of UK Research and Innovation); the Health Foundation; Macmillan Cancer Support; and UCB Pharma. ADa and JJD are also NIHR Senior Investigators. The views expressed in this article are those of the author(s) and not necessarily those of the NIHR, or the Department of Health and Social Care. DM is supported by a University of Ottawa Research Chair. MKEZ is supported by the US Food and Drug Administration (FDA), and DP is supported in part by the Office of the Director at the National Library of Medicine (NLM), US National Institutes of Health (NIH). AB is supported by an NIH award 7K01HL141771-02. PAK received grants from UKRI Future Leaders Fellowship and from Moorfields Eye Charity Career Development Award. SJV received funding from the Engineering and Physical Sciences Research Council, UK Research and Innovation (UKRI), Accenture, Warwick Impact Fund, Health Data Research UK, and European Regional Development Fund. SR is an employee of the UKRI. This article may not be consistent with NIH and/or FDA's views or policies. It reflects only the views and opinions of the authors.

Publisher Copyright:
© 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license

ASJC Scopus subject areas

  • Decision Sciences (miscellaneous)
  • Health Information Management
  • Health Informatics
  • Medicine (miscellaneous)


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