{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,15]],"date-time":"2026-08-15T00:10:31Z","timestamp":1786752631875,"version":"build-2736575974"},"reference-count":24,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Central Research Institute Public Project","award":["GY2024D-1, GY2024Z-1"],"award-info":[{"award-number":["GY2024D-1, GY2024Z-1"]}]},{"name":"Shanghai Yangfan Special Programme","award":["23YF1448700"],"award-info":[{"award-number":["23YF1448700"]}]},{"name":"Shanghai Key Laboratory of Forensic Medicine","award":["21DZ2270800"],"award-info":[{"award-number":["21DZ2270800"]}]},{"name":"Shanghai Forensic Service Platform, Key Laboratory of Forensic Science, Ministry of Justice, the Project of Shanghai Association of Forensic Science","award":["SHSFJD2023-008"],"award-info":[{"award-number":["SHSFJD2023-008"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:sec id=\"sec001\">\n<jats:title>Background and Objective<\/jats:title>\n<jats:p>Systematic reviews and meta-analyses are critical in forensic medicine; however, these processes are labor-intensive and time-consuming. ASReview, an open-source machine learning framework, has demonstrated potential to improve the efficiency and transparency of systematic reviews in other disciplines. Nevertheless, its applicability to forensic medicine remains unexplored. This study evaluates the utility of ASReview for forensic medical literature review.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec002\">\n<jats:title>Methods<\/jats:title>\n<jats:p>A three-stage experimental design was implemented. First, stratified five-fold cross-validation was conducted to assess ASReview\u2019s compatibility with forensic medical literature. Second, incremental learning and sampling methods were employed to analyze the model\u2019s performance on imbalanced datasets and the effect of training set size on predictive accuracy. Third, gold standard were translated into computational languages to evaluate ASReview\u2019s capacity to address real-world systematic review objectives.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec003\">\n<jats:title>Results<\/jats:title>\n<jats:p>ASReview exhibited robust viability for screening forensic medical literature. The tool efficiently prioritized relevant studies while excluding irrelevant records, thereby improving review productivity. Model performance remained stable when labeled training data constituted less than 80% of the total sample size. Notably, when the training set proportion ranged from 10% to 55%, ASReview\u2019s predictions aligned closely with human reviewer decisions.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec004\">\n<jats:title>Conclusion<\/jats:title>\n<jats:p>ASReview represents a promising tool for forensic medical literature review. Its ability to handle imbalanced datasets and gather goal-oriented information enhances the efficiency and transparency of systematic reviews and meta-analyses in forensic medicine. Further research is required to optimize implementation strategies and validate its utility across diverse forensic medical contexts.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pone.0329349","type":"journal-article","created":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T17:30:32Z","timestamp":1754069432000},"page":"e0329349","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":4,"title":["An open-source interactive AI framework for assisting automatic literature review in forensic medicine: Focus on brain injury mechanisms"],"prefix":"10.1371","volume":"20","author":[{"given":"Ya-Wen","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong-Hua","family":"Zou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"He-Wen","family":"Dong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan-Yuan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"En-Hao","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi-Ling","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2025-3254","authenticated-orcid":true,"given":"Ning-Guo","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,8,1]]},"reference":[{"key":"pone.0329349.ref001","doi-asserted-by":"crossref","first-page":"2215","DOI":"10.1002\/asi.23329","article-title":"Growth rates of modern science: a bibliometric analysis based on the number of publications and cited references","volume":"66","author":"L Bornmann","year":"2015","journal-title":"J Assoc Inf Sci Technol"},{"issue":"3","key":"pone.0329349.ref002","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1111\/1468-0009.12210","article-title":"The mass production of redundant, misleading, and conflicted systematic reviews and meta-analyses","volume":"94","author":"JPA Ioannidis","year":"2016","journal-title":"Milbank Q"},{"issue":"1","key":"pone.0329349.ref003","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/2046-4053-4-5","article-title":"Using text mining for study identification in systematic reviews: a systematic review of current approaches","volume":"4","author":"A O\u2019Mara-Eves","year":"2015","journal-title":"Syst Rev"},{"issue":"1","key":"pone.0329349.ref004","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1186\/s13643-019-1074-9","article-title":"Toward systematic review automation: a practical guide to using machine learning tools in research synthesis","volume":"8","author":"IJ Marshall","year":"2019","journal-title":"Syst Rev"},{"issue":"7553","key":"pone.0329349.ref005","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"Y LeCun","year":"2015","journal-title":"Nature"},{"issue":"1","key":"pone.0329349.ref006","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1186\/s13643-019-0942-7","article-title":"Machine learning algorithms for systematic review: reducing workload in a preclinical review of animal studies and reducing human screening error","volume":"8","author":"A Bannach-Brown","year":"2019","journal-title":"Syst Rev"},{"issue":"2","key":"pone.0329349.ref007","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1038\/s42256-020-00287-7","article-title":"An open source machine learning framework for efficient and transparent systematic reviews","volume":"3","author":"R van de Schoot","year":"2021","journal-title":"Nat Mach Intell"},{"key":"pone.0329349.ref008","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"G Litjens","year":"2017","journal-title":"Med Image Anal"},{"issue":"4","key":"pone.0329349.ref009","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1016\/j.jmir.2019.09.005","article-title":"Machine learning and deep learning in medical imaging: intelligent imaging","volume":"50","author":"G Currie","year":"2019","journal-title":"J Med Imaging Radiat Sci"},{"issue":"2","key":"pone.0329349.ref010","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1002\/jrsm.1378","article-title":"Which academic search systems are suitable for systematic reviews or meta-analyses? 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