ArticleEClinicalMedicine2025
Discrepancies in reported results between trial registries and journal articles for AI clinical research.
Article in EClinicalMedicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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.
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.
Who cites it
5 citing papers in PubMed.
- Clinical registry metadata as a hidden bottleneck in AI-driven drug discovery: a computational audit of translational phase data in glioma research.Scientific reports · 2026Article
- Clinical outcomes and reporting quality of large language model interventions in practice: a systematic evidence map.NPJ digital medicine · 2026Article
- A literature review and reflection on clinical trials: current challenges, future directions, and potential strategies to overcome practical barriers.AME clinical trials review · 2026Article
- Answer to: Commentary on "Can AI Answer My Questions? Utilizing Artificial Intelligence in the Perioperative Assessment for Abdominoplasty Patients".Aesthetic plastic surgery · 2025Article
- Potential Obstacles and Strategic Interventions for Addressing Gaps in the Understanding of Human-Artificial Intelligence Interactions in Medical Practice.Korean journal of radiology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Complete and unbiased reporting of clinical trial results is essential for evaluating medical advances, yet publication bias and reporting discrepancies in research on the clinical application of artificial intelligence (AI) remain unknown. Methods: We conducted a comprehensive search of research publications and clinical trial registries focused on the application of AI in healthcare. Our search included publications in Dimensions.ai and pre-registered records from ClinicalTrials.gov and the EU Clinical Trials Registry before 31 December 2023. We linked registered trials to their corresponding publications, analysed the registration, reporting and different dissemination patterns of results, identified discrepancies between clinical trial registries and published literature, and assessed the use of these results in secondary research. Findings: We identified 28,248 publications related to the use of AI in clinical settings and found 1863 publications that included a clinical trial registration ID. The clinical trial registry search identified 3710 trials evaluating the use of AI in clinical settings, of which 1106 trials are completed, yet only 101 trials have published results. By linking the trials to their corresponding publications, we found that 26 trials had results available from both registries and publications. There were more results in trial registries than in articles, but researchers showed a clear preference for rapid dissemination of results through peer-reviewed articles (37.6% published within one year) over trial registries (15.8%). Discrepancies and omissions of results were common, and no complete agreement was observed between the two sources. Selective reporting of publications occurred in 53.6% of cases, and the underestimation of the incidence of adverse events is alarming. Interpretation: This research uncovers concerns with the registration and reporting of AI clinical trial results. While trial registries and publications serve distinct yet complementary roles in disseminating research findings, discrepancies between them may undermine the reliability of the evidence. We emphasise adherence to guidelines that promote transparency and standardisation of reporting, especially for investigator-initiated trials (IITs). Funding: The authors declare no source of funding.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.