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

Liu, Xiaoxuan, Rivera, Samantha Cruz, Moher, David, Calvert, Melanie J and Denniston, Alastair K (2020) Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI Extension. BMJ (Clinical research ed.), 370. m3164. ISSN 1756-1833.

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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.

Item Type: Article
Subjects: QA Mathematics. Computing
WW Eyes. Ophthalmology
Divisions: Ambulatory Care > Ophthalmology
Related URLs:
Depositing User: Mr Philip O'Reilly
Date Deposited: 23 Sep 2020 14:01
Last Modified: 23 Sep 2020 14:01
URI: http://www.repository.uhblibrary.co.uk/id/eprint/3470

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