Evidence map›Paper›PMID 40117574›Full record

ArticleJournal of medical Internet research2025

Public Disclosure of Results From Artificial Intelligence/Machine Learning Research in Health Care: Comprehensive Analysis of ClinicalTrials.gov, PubMed, and Scopus Data (2010-2023).

Shoko Maru, Ryohei Kuwatsuru, Michael D Matthias, Ross J Simpson

Erratum issuedAbstract read
In one paragraph

Article in Journal of medical Internet research, 2025. 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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Shoko MaruReal‑World Evidence and Data Assessment (READS), Graduate School of Medicine, Juntendo University, Tokyo, Japan.ORCID 0000-0002-4722-4111
Ryohei KuwatsuruReal‑World Evidence and Data Assessment (READS), Graduate School of Medicine, Juntendo University, Tokyo, Japan.ORCID 0000-0003-1960-6400
Michael D MatthiasMatthias IT Pty Ltd, Brisbane, Australia.ORCID 0009-0005-9139-618X
Ross J SimpsonDivision of Cardiology, School of Medicine, University of North Carolina Chapel Hill, Chapel Hill, NC, United States.ORCID 0000-0001-6938-8435

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDespite the rapid growth of research in artificial intelligence/machine learning (AI/ML), little is known about how often study results are disclosed years after study completion.

objectiveWe aimed to estimate the proportion of AI/ML research that reported results through ClinicalTrials.gov or peer-reviewed publications indexed in PubMed or Scopus.

methodsUsing data from the Clinical Trials Transformation Initiative Aggregate Analysis of ClinicalTrials.gov, we identified studies initiated and completed between January 2010 and December 2023 that contained AI/ML-specific terms in the official title, brief summary, interventions, conditions, detailed descriptions, primary outcomes, or keywords. For 842 completed studies, we searched PubMed and Scopus for publications containing study identifiers and AI/ML-specific terms in relevant fields, such as the title, abstract, and keywords. We calculated disclosure rates within 3 years of study completion and median times to disclosure-from the "primary completion date" to the "results first posted date" on ClinicalTrials.gov or the earliest date of journal publication.

resultsWhen restricted to studies completed before 2021, ensuring at least 3 years of follow-up in which to report results, 7.0% (22/316) disclosed results on ClinicalTrials.gov, 16.5% (52/316) in journal publications, and 20.6% (65/316) through either route within 3 years of completion. Higher disclosure rates were observed for trials: 11.0% (15/136) on ClinicalTrials.gov, 25.0% (34/136) in journal publications, and 30.1% (41/136) through either route. Randomized controlled trials had even higher disclosure rates: 12.2% (9/74) on ClinicalTrials.gov, 31.1% (23/74) in journal publications, and 36.5% (27/74) through either route. Nevertheless, most study findings (79.4%; 251/316) remained undisclosed 3 years after study completion. Trials using randomization (vs nonrandomized) or masking (vs open label) had higher disclosure rates and shorter times to disclosure. Most trials (85%; 305/357) had sample sizes of ≤1000, yet larger trials (n>1000) had higher publication rates (30.8%; 16/52) than smaller trials (n≤1000) (17.4%; 53/305). Hospitals (12.4%; 42/340), academia (15.1%; 39/259), and industry (13.7%; 20/146) published the most. High-income countries accounted for 82.4% (89/108) of all published studies. Of studies with disclosed results, the median times to report through ClinicalTrials.gov and in journal publications were 505 days (IQR 399-676) and 407 days (IQR 257-674), respectively. Open-label trials were common (60%; 214/357). Single-center designs were prevalent in both trials (83.3%; 290/348) and observational studies (82.3%; 377/458).

conclusionsFor nearly 80% of completed studies, findings remained undisclosed within the 3 years of follow-up, raising questions about the representativeness of publicly available evidence. While methodological rigor was generally associated with higher publication rates, the predominance of single-center designs and high-income countries may limit the generalizability of the results currently accessible.

Indexed as

Artificial IntelligenceDelivery of Health CareDisclosureMachine LearningClinical Trials as TopicHumansPubMedAIalgorithmanalyticsartificial intelligenceclinical trialClinicalTrials.govdatabasedata-source disparitiesdeep learningdisparitydisseminationhealth carehealth disparitiesmachine learningMLmodelpublic disclosurepublic disclosure of resultsPubMedScopussocial disparitysocial inequalitysocial inequity

Identifiers

PMID40117574
PMCPMC11971578

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.