ArticleJournal of the American Medical Informatics Association : JAMIA2025
RAPID: Reliable and efficient Automatic generation of submission rePortIng checklists with large language moDels.
Article in Journal of the American Medical Informatics Association : JAMIA, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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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.
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Who cites it
4 citing papers in PubMed.
- Agreement between large language models, human reviewers, and authors in evaluating STROBE checklists for observational studies in rheumatology.EULAR rheumatology open · 2026Article
- Article
- The LEADING guideline: Reporting standards for expert panel, best-estimate diagnosis, and longitudinal expert all data (LEAD) methods.Comprehensive psychiatry · 2025Article
- Large Language Models and the Analyses of Adherence to Reporting Guidelines in Systematic Reviews and Overviews of Reviews (PRISMA 2020 and PRIOR).Journal of medical systems · 2025Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
objectiveTo evaluate an automated reporting checklist generation tool using large language models and retrieval augmentation generation technology, called RAPID. MATERIALS AND
methodsThis study utilized large language models to develop a retrieval augmentation generation architecture. To assess its performance, a total of 91 published journal articles were collected and manually annotated in accordance with the CONSORT and CONSORT-AI medical reporting guidelines. These articles comprised 50 randomized controlled trials conducted without AI intervention and 41 randomized controlled trials that incorporated AI tools.
resultsFifty RCT articles without the intervention of AI tools and 41 RCT articles with the intervention of AI tools were collected as CONSORT and CONSORT-AI datasets. All of the CONSORT reporting items (37) were included in the tool. RAPID achieved a high average accuracy rate of 92.11% and a content consistency score of 81.14% on the CONSORT dataset. Of the CONSORT-AI reporting items, 11 items related to the intervention of AI tools were included in the tool. RAPID achieved an average accuracy of 83.81% with a content consistency score of 72.51% on the CONSORT-AI dataset. DISCUSSION: RAPID may effectively save time and improve working efficiency for different user groups such as medical authors, researchers, editors, and reviewers.
conclusionRAPID has strong scalability, which can be easily adapted to different medical reporting guidelines without transfer learning on a large dataset. RAPID got state-of-the-art performance on 2 datasets for 2 different checklists compared to other methods.
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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.