Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension
Open accessAbstract
The CONSORT 2010 statement provides minimum guidelines for reporting randomised trials. Its widespread use has been instrumental in ensuring transparency in the 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 impact on health outcomes. The CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence) 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 (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving 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 upon in a two-day consensus meeting (31 stakeholders), and refined through a checklist pilot (34 participants). The CONSORT-AI extension includes 14 new items that 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 provision of an 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.
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There are concerns about the quality and transparency of published clinical trials in medical AI, which often lack the methodological rigor and detailed reporting necessary to inspire clinical confidence and enable replication.
“Second, there are also concerns about the quality and transparency of published clinical trials, which often lack the methodological rigor and detailed reporting necessary to inspire clinical confidence and enable replication9–11.”
From A quantitative analysis of global AI medical studies: gaps in randomized controlled trials (2026) · cites this paper as [10]
The trial findings are reported according to CONSORT guidelines and extensions for AI, cluster-randomized trials, and pragmatic trials.
“We report our trial findings according to the CONSORT guidelines31 and extensions for AI32, cluster-randomized trials33 and pragmatic trials34 (Supplementary Table 1).”
From Real-time surveillance system for patient deterioration: a pragmatic cluster-randomized controlled trial (2025) · cites this paper as [32]
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“…should follow recognised reporting frameworks, including Consolidated Standards of Reporting Trials-AI (CONSORT-AI) for clinical trial reports and Standard Protocol Items: Recommendations for Interventional Trials-AI (SPIRIT-AI) for clinical trial protocols involving AI interventions [46,47].”
- Authors
- Liu, X., Cruz Rivera, S., Moher, D., Calvert, M. J., Denniston, A. K., Ashrafian, H., Beam, A. L., Chan, A.-W., Collins, G. S., Deeks, A. D. J., ElZarrad, M. K., Espinoza, C., Esteva, A., Faes, L., Ferrante di Ruffano, L., Fletcher, J., Golub, R., Harvey, H., Haug, C., Holmes, C., Jonas, A., Keane, P. A., Kelly, C. J., Lee, A. Y., Lee, C. S., Manna, E., Matcham, J., McCradden, M., Monteiro, J., Mulrow, C., Oakden-Rayner, L., Paltoo, D., Panico, M. B., Price, G., Rowley, S., Savage, R., Sarkar, R., Vollmer, S. J., & Yau, C.
- Affiliations
- University Hospitals Birmingham NHS Foundation Trust (GB)
- University of Ottawa (CA)
- Corresponding author
- Denniston, Alastair K
- ORCID
- Liu, Xiaoxuan
- Moher, David
- Calvert, Melanie J
- Denniston, Alastair K
- Ashrafian, Hutan
- Beam, Andrew L
- Chan, An-Wen
- ElZarrad, M Khair
- Esteva, Andre
- Faes, Livia
- Fletcher, John
- Harvey, Hugh
- Haug, Charlotte
- Keane, Pearse A
- Kelly, Christopher J
- Lee, Aaron Y
- McCradden, Melissa
- Mulrow, Cynthia
- Rowley, Samuel
- Savage, Richard
- Sarkar, Rupa
- Vollmer, Sebastian J
- Yau, Christopher
- Venue
- The Lancet Digital Health
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 2
- Issue
- 10
- Pages
- e537-e548
- ISSN
- 2589-7500 (ISSN-L)
- PMC
- PMC8183333
- OpenAlex
- W3136907493
- Cited by
- 555
- References
- 63
- Citations by year
- 2026: 257 · 2025: 103 · 2024: 81 · 2023: 50 · 2022: 38 · 2021: 23 · 2020: 3
- FWCI
- 5.87
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- The Lancet Digital Health (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- University of Birmingham Research Portal (University of Birmingham) (publishedVersion, cc-by-nc-nd)
- PubMed Central (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- University of Birmingham Research Portal (University of Birmingham) (submittedVersion, cc-by-nc-nd)
- Funders
- Research England
- Wellcome Trust
- Alan Turing Institute
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- CONSORT-AI, reporting guidelines, artificial intelligence interventions, clinical trial reporting, randomised trials, human-AI interaction, transparency, risk of bias, consensus development
- MeSH terms
- Artificial Intelligence, Clinical Trials as Topic, Delphi Technique, Humans, Publishing, Research Design, Guidelines as Topic, Disclosure, Consensus, Checklist, Research Report
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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