Evidence map›Paper›PMID 35585198›Full record

GuidelineNature medicine2022

Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI.

Baptiste Vasey, Myura Nagendran, Bruce Campbell, David A Clifton, Gary S Collins, Spiros Denaxas, Alastair K Denniston, Livia Faes, Bart Geerts, Mudathir Ibrahim and 14 more

Erratum issuedAbstract readReviewPractice Guideline
PubMed Publisher
In one paragraph

Guideline in Nature medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 457 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
457citing papers in PubMed, 6 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

457 citing papers in PubMed, 6 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Pooled it
  7. Trial
  8. Trial
  9. Article
  10. Observational
  11. Article
  12. Review
  13. Article
  14. Same-Patient, Same-Time Point Evidence for Radiomics Benchmarking.Journal of medical Internet research · 2026
    Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

397 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

24 authors.

Baptiste VaseyNuffield Department of Surgical Sciences, University of Oxford, Oxford, UK. baptiste.vasey@gmail.com.ORCID http://orcid.org/0000-0002-0017-8891
Myura NagendranUKRI Centre for Doctoral Training in AI for Healthcare, Imperial College London, London, UK.
Bruce CampbellUniversity of Exeter Medical School, Exeter, UK.
David A CliftonInstitute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK.
Gary S CollinsCentre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology & Musculoskeletal Sciences, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0002-2772-2316
Spiros DenaxasInstitute of Health Informatics, University College London, London, UK.
Alastair K DennistonUniversity Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.ORCID http://orcid.org/0000-0001-7849-0087
Livia FaesMoorfields Eye Hospital NHS Foundation Trust, London, UK.
Bart GeertsHealthplus.ai-R&D BV, Amsterdam, The Netherlands.
Mudathir IbrahimNuffield Department of Surgical Sciences, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0002-6345-1533
Xiaoxuan LiuUniversity Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.
Bilal A MateenInstitute of Health Informatics, University College London, London, UK.ORCID http://orcid.org/0000-0003-4423-6472
Piyush MathurDepartment of General Anesthesiology, Anesthesiology Institute, Cleveland Clinic, Cleveland, OH, USA.
Melissa D McCraddenThe Hospital for Sick Children, Toronto ON, Canada.ORCID http://orcid.org/0000-0002-6476-2165
Lauren MorganMorgan Human Systems Ltd, Shrewsbury, UK.
Johan OrdishMedicines and Healthcare products Regulatory Agency, London, UK.ORCID http://orcid.org/0000-0001-6911-2367
Campbell RogersHeartFlow Inc., Redwood City, CA, USA.ORCID http://orcid.org/0000-0003-3955-7650
Suchi SariaDepartments of Computer Science, Statistics, and Health Policy, and Division of Informatics, Johns Hopkins University, Baltimore, MD, USA.
Daniel S W TingSingapore National Eye Center, Singapore Eye Research Institute, Singapore, Singapore.
Peter WatkinsonCritical Care Research Group, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0003-1023-3927
Wim WeberThe BMJ, London, UK.ORCID http://orcid.org/0000-0002-7498-5717
Peter WheatstoneSchool of Medicine, University of Leeds, Leeds, UK.
Peter McCullochNuffield Department of Surgical Sciences, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0002-3210-8273
DECIDE-AI expert group

Funding

Predicting Pulmonary and Cardiac Morbidity in Preterm Infants with Deep LearningK01HL141771 · NHLBI · HARVARD SCHOOL OF PUBLIC HEALTH · PI BEAM, ANDREW L. · 2019 to 2023
$832k
Cancer Research UK 27294Cancer Research UK C49297/A27294Department of HealthMedical Research Council MC_PC_19005Medical Research Council MC_PC_20059Medical Research Council MR/K006584/1Medical Research Council MR/T019050/1NHLBI NIH HHS K01 HL141771Wellcome Trust
6 · The paper itself

Abstract

A growing number of artificial intelligence (AI)-based clinical decision support systems are showing promising performance in preclinical, in silico evaluation, but few have yet demonstrated real benefit to patient care. Early-stage clinical evaluation is important to assess an AI system's actual clinical performance at small scale, ensure its safety, evaluate the human factors surrounding its use and pave the way to further large-scale trials. However, the reporting of these early studies remains inadequate. The present statement provides a multi-stakeholder, consensus-based reporting guideline for the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI). We conducted a two-round, modified Delphi process to collect and analyze expert opinion on the reporting of early clinical evaluation of AI systems. Experts were recruited from 20 pre-defined stakeholder categories. The final composition and wording of the guideline was determined at a virtual consensus meeting. The checklist and the Explanation & Elaboration (E&E) sections were refined based on feedback from a qualitative evaluation process. In total, 123 experts participated in the first round of Delphi, 138 in the second round, 16 in the consensus meeting and 16 in the qualitative evaluation. The DECIDE-AI reporting guideline comprises 17 AI-specific reporting items (made of 28 subitems) and ten generic reporting items, with an E&E paragraph provided for each. Through consultation and consensus with a range of stakeholders, we developed a guideline comprising key items that should be reported in early-stage clinical studies of AI-based decision support systems in healthcare. By providing an actionable checklist of minimal reporting items, the DECIDE-AI guideline will facilitate the appraisal of these studies and replicability of their findings.

Indexed as

Artificial IntelligenceResearch DesignChecklistConsensusHumansResearch Report

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.