TY - JOUR
T1 - Guidelines for clinical trial protocols for interventions involving artificial intelligence
T2 - the SPIRIT-AI extension
AU - Cruz Rivera, Samantha
AU - Liu, Xiaoxuan
AU - Chan, An Wen
AU - Denniston, Alastair K.
AU - Calvert, Melanie J.
AU - Ashrafian, Hutan
AU - Beam, Andrew L.
AU - Collins, Gary S.
AU - Darzi, Ara
AU - Deeks, Jonathan J.
AU - ElZarrad, M. Khair
AU - Espinoza, Cyrus
AU - Esteva, Andre
AU - Faes, Livia
AU - Ferrante di Ruffano, Lavinia
AU - Fletcher, John
AU - Golub, Robert
AU - Harvey, Hugh
AU - Haug, Charlotte
AU - Holmes, Christopher
AU - Jonas, Adrian
AU - Keane, Pearse A.
AU - Kelly, Christopher J.
AU - Lee, Aaron Y.
AU - Lee, Cecilia S.
AU - Manna, Elaine
AU - Matcham, James
AU - McCradden, Melissa
AU - Moher, David
AU - Monteiro, Joao
AU - Mulrow, Cynthia
AU - Oakden-Rayner, Luke
AU - Paltoo, Dina
AU - Panico, Maria Beatrice
AU - Price, Gary
AU - Rowley, Samuel
AU - Savage, Richard
AU - Sarkar, Rupa
AU - Vollmer, Sebastian J.
AU - Yau, Christopher
N1 - 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.
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
PY - 2020/10
Y1 - 2020/10
N2 - 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.
AB - 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.
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U2 - 10.1016/S2589-7500(20)30219-3
DO - 10.1016/S2589-7500(20)30219-3
M3 - Review article
C2 - 33328049
AN - SCOPUS:85091204921
SN - 2589-7500
VL - 2
SP - e549-e560
JO - The Lancet Digital Health
JF - The Lancet Digital Health
IS - 10
ER -
