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Machine learning (ML) offers new pathways to overcome these challenges by integrating multi-source real-world data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We systematically reviewed English-language studies from database inception to June 2026. Searches were performed in PubMed, Embase, and Web of Science. Two reviewers independently screened records. Given substantial heterogeneity across ML tasks (signal detection, text extraction, risk prediction, prognosis stratification), algorithms, data sources, and metrics, we performed narrative synthesis. Risk of bias was assessed using adapted QUADAS-2.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We identified 43 studies. For adverse-event prediction, tree-based models reported AUCs of 0.85\u20130.87, though estimates derive from heterogeneous settings. NLP reduced redundant signals by 17% in vaccine reporting systems. For myocarditis, ML models reached AUCs up to 0.899 in cardiovascular cohorts, but direct mRNA vaccine applications remain limited and retrospective. Emerging platforms (self-amplifying and tumor mRNA vaccines) lack post-marketing data, rendering ML applications largely conceptual.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>ML-assisted pharmacovigilance enables a shift from passive to active, intelligent monitoring. Despite challenges in data quality, model interpretability, and regulatory approval, intelligent pharmacovigilance systems will become essential infrastructure for safeguarding public health.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12911-026-03832-3","type":"journal-article","created":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T02:04:03Z","timestamp":1788919443000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning-assisted mRNA vaccine pharmacovigilance: a systematic review of multi-source real-world data"],"prefix":"10.1186","volume":"26","author":[{"given":"YuLong","family":"He","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Mao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"XinYu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,9]]},"reference":[{"issue":"2","key":"3832_CR1","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1016\/j.immuni.2005.06.008","volume":"23","author":"K Karik\u00f3","year":"2005","unstructured":"Karik\u00f3 K, Buckstein M, Ni H, Weissman D. 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