Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension

The SPIRIT-AI and CONSORT-AI Working Group, SPIRIT-AI and CONSORT-AI Steering Group, SPIRIT-AI and CONSORT-AI Consensus Group, Samantha Cruz Rivera, Xiaoxuan Liu, An-Wen Chan, Alastair Denniston, Melanie Calvert

Research output: Contribution to journalArticlepeer-review

34 Citations (Scopus)
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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.
Original languageEnglish
Article number1351–1363
Pages (from-to)1351-1363
Number of pages13
JournalNature Medicine
Issue number9
Publication statusPublished - 9 Sept 2020

Bibliographical note

Funding Information:
We thank the participants who were involved in the Delphi study and Pilot study (Supplementary Note); E. Marston (University of Birmingham, UK) for strategic support; and C. Radovanovic (University Hospitals Birmingham NHS Foundation Trust, UK) and A. 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. M.J.C. is a National Institute for Health Research (NIHR) Senior Investigator and receives funding from the National Institute for Health Research (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. A.D. and J.D. 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. S.J.V. receives 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. S.R. is an employee of the Medical Research Council (UKRI). D.M. is supported by a University of Ottawa Research Chair. A.B. is supported by a National Institutes of Health (NIH) award (7K01HL141771-02). M.K.E. is supported by the U.S. Food and Drug Administration (FDA), and D.P. is supported in part by the Office of the Director at the National Library of Medicine (NLM), US National Institutes of Health (NIH). 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).


  • Artificial Intelligence
  • Clinical Trials as Topic/methods
  • Humans
  • Research Design/standards

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)


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