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Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines

Authors :
Alexander P. L. Martindale
Carrie D. Llewellyn
Richard O. de Visser
Benjamin Ng
Victoria Ngai
Aditya U. Kale
Lavinia Ferrante di Ruffano
Robert M. Golub
Gary S. Collins
David Moher
Melissa D. McCradden
Lauren Oakden-Rayner
Samantha Cruz Rivera
Melanie Calvert
Christopher J. Kelly
Cecilia S. Lee
Christopher Yau
An-Wen Chan
Pearse A. Keane
Andrew L. Beam
Alastair K. Denniston
Xiaoxuan Liu
Source :
Nature Communications, Vol 15, Iss 1, Pp 1-11 (2024)
Publication Year :
2024
Publisher :
Nature Portfolio, 2024.

Abstract

Abstract The Consolidated Standards of Reporting Trials extension for Artificial Intelligence interventions (CONSORT-AI) was published in September 2020. Since its publication, several randomised controlled trials (RCTs) of AI interventions have been published but their completeness and transparency of reporting is unknown. This systematic review assesses the completeness of reporting of AI RCTs following publication of CONSORT-AI and provides a comprehensive summary of RCTs published in recent years. 65 RCTs were identified, mostly conducted in China (37%) and USA (18%). Median concordance with CONSORT-AI reporting was 90% (IQR 77–94%), although only 10 RCTs explicitly reported its use. Several items were consistently under-reported, including algorithm version, accessibility of the AI intervention or code, and references to a study protocol. Only 3 of 52 included journals explicitly endorsed or mandated CONSORT-AI. Despite a generally high concordance amongst recent AI RCTs, some AI-specific considerations remain systematically poorly reported. Further encouragement of CONSORT-AI adoption by journals and funders may enable more complete adoption of the full CONSORT-AI guidelines.

Subjects

Subjects :
Science

Details

Language :
English
ISSN :
20411723
Volume :
15
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Nature Communications
Publication Type :
Academic Journal
Accession number :
edsdoj.2d27b1908784851a58e618b7b71f7b0
Document Type :
article
Full Text :
https://doi.org/10.1038/s41467-024-45355-3