Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension

By An-Wen Chan, A. Darzi, S. Cruz Rivera, Christopher Holmes, Christopher Yau, D. Moher, A. Denniston, H. Ashrafian, J. J. Deeks, L. F. D. Ruffano and 34 more. 2020

1,116 citations recorded by the source corpus.

The CONSORT 2010 statement provides minimum guidelines for reporting randomized 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. The CONSORT-AI and SPIRIT-AI extensions improve the transparency of clinical trial design and trial protocol reporting for artificial intelligence interventions.

Subfields: Economics and Econometrics, Health Informatics, Statistics and Probability, Evaluation & Benchmarks

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Citations in the corpus

Underspecification Presents Challenges for Credibility in Modern Machine Learning (2020, background): “Stress tests are becoming a key part of standards of evidence in a number of applied domains, including medicine (Collins et al., 2015; Liu et al., 2020a; Rivera et al., 2020), economics (Mullainathan and Spiess, 2017; Athey, 2017), public policy (Kleinberg et al., 2015), and epidemiology (Hoffmann…”
The TRIPOD-LLM reporting guideline for studies using large language models (2025, neutral): “This is in addition to other guidelines that offer complementary guidance on AI development throughout the model life cycle 12–14 .”
Shifting machine learning for healthcare from development to deployment and from models to data (2022, background): “Current regulation and approval processes are being adapted to meet the emerging needs; in particular, initiatives are attempting to address data shifts and patient representation in the training datasets 165,180,181 .”
Harnessing multimodal data integration to advance precision oncology (2021, methodology): “SPIRIT-AI and CONSORT-AI are consensus guidelines for AI in clinical trial protocols and reports, respectively, that extend the SPIRIT and CONSORT guidelines for randomized clinical trials 134–136 .”
Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI (2022, methodology): “It is seen as an approach within the field of AI’ 26 .”
The Fallacy of AI Functionality (2022, background): “In fact, healthcare is the ﬁeld where AI product failures appear to be the most visible, in part due to the rigor of pre-established evaluation processes [20, 119, 160, 205].”
The next generation of evidence-based medicine (2023, positive): “…Intelligence (SPIRIT-AI) extension) and publications (Consolidated Standards of Reporting Trials–Artificial Intelligence (CONSORT-AI) extension) 79,80 are intended to achieve standardized and transparent reporting for randomized clinical trials involving AI, and these are just the beginning of…”
Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022, methodology): “…into the trial setting is highly recommended, including onsite and offsite requirements, version number and other technical speci ﬁ cations 28,64,65 , but also the human-AI interaction involved (e.g., assistive versus directive, see phase 4) 46,64 and the patient treatment strategy…”
Deep learning-enabled medical computer vision (2021, background): “Excitingly, AI and CV can also help support clinical trials 153,154 through a number of applications — including patient selection, tumor tracking, adverse event detection, etc — creating an ecosystem in which AI can help design safe AI.”