{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T05:56:44Z","timestamp":1775282204464,"version":"3.50.1"},"reference-count":32,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T00:00:00Z","timestamp":1750118400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"name":"Young Collaborative Research","award":["C2004-23Y"],"award-info":[{"award-number":["C2004-23Y"]}]},{"DOI":"10.13039\/501100005847","name":"Health and Medical Research Fund","doi-asserted-by":"publisher","award":["11221026"],"award-info":[{"award-number":["11221026"]}],"id":[{"id":"10.13039\/501100005847","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangdong-Hong Kong Technology Cooperation Funding Scheme","award":["GHX\/133\/20SZ"],"award-info":[{"award-number":["GHX\/133\/20SZ"]}]},{"name":"Research Unit of Evidence-Based Evaluation and Guidelines, Chinese Academy of Medical Sciences","award":["2021RU017"],"award-info":[{"award-number":["2021RU017"]}]},{"name":"Vincent and Lily Woo Foundation"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To evaluate an automated reporting checklist generation tool using large language models and retrieval augmentation generation technology, called RAPID.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>This 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Fifty 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>RAPID may effectively save time and improve working efficiency for different user groups such as medical authors, researchers, editors, and reviewers.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>RAPID 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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jamia\/ocaf093","type":"journal-article","created":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T07:34:14Z","timestamp":1748331254000},"page":"1340-1349","source":"Crossref","is-referenced-by-count":3,"title":["RAPID: Reliable and efficient Automatic generation of submission rePortIng checklists with large language moDels"],"prefix":"10.1093","volume":"32","author":[{"given":"Zeming","family":"Li","sequence":"first","affiliation":[{"name":"Department of Computer Science, Hong Kong Baptist University , Hong Kong SAR 999077,","place":["China"]}]},{"given":"Xufei","family":"Luo","sequence":"additional","affiliation":[{"name":"Research Unit of Evidence-Based Evaluation and Guidelines, Chinese Academy of Medical Sciences (2021RU017), School of Basic Medical Sciences, Lanzhou University , Lanzhou 730000,","place":["China"]}]},{"given":"Zhenhua","family":"Yang","sequence":"additional","affiliation":[{"name":"Vincent V.C. 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