Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension
File(s)Guidelines - HUTAN.pdf (600.06 KB)
Published version
Author(s)
Type
Journal Article
Abstract
The SPIRIT 2013 statement aims to improve the completeness of clinical trial protocol reporting by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent 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 their impact on health outcomes. The SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trial protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 26 candidate items, which were consulted upon by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items that were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-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 will be integrated, considerations for the handling of input and output data, the human–AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer reviewers, as well as the general readership, to understand, interpret, and critically appraise the design and risk of bias for a planned clinical trial.
Date Issued
2020-10
Date Acceptance
2020-09-01
Citation
The Lancet Digital Health, 2020, 2 (10), pp.e549-e560
ISSN
2589-7500
Publisher
Elsevier BV
Start Page
e549
End Page
e560
Journal / Book Title
The Lancet Digital Health
Volume
2
Issue
10
Copyright Statement
© 2020 The Author(s). Published by Elsevier Ltd. This is
an Open Access article under the CC BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
an Open Access article under the CC BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
Sponsor
National Institute of Health Research
Identifier
https://www.sciencedirect.com/science/article/pii/S2589750020302193?via%3Dihub
Publication Status
Published
Date Publish Online
2020-09-09