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Inclusion criteria targeted quantitative studies using ML\/AI for candidates, donors, or recipients. Two reviewers assessed eligibility and extracted data, following PRISMA guidelines.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>A total of 20 papers were included, synthesizing results into five major categories. Eight studies were led by a Spanish team, focusing on donor-recipient matching and proposing machine learning models to predict post- LT survival. Other international studies addressed organ supply-demand issues and developed predictive models to optimize LT outcomes. The studies highlight the potential of ML\/AI to enhance LT allocation and outcomes. Despite advancements, limitations included the lack of robust transplant-related benefit models and improvements in urgency models compared to MELD.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Discussion<\/jats:title>\n            <jats:p>This review highlighted the potential of AI and ML to enhance liver transplant allocation and outcomes. Significant advancements were noted, but limitations such as the need for better urgency models and the absence of a transplant-related benefit model remain. Most studies emphasized utility, focusing on survival outcomes. Future research should address the interpretability and generalizability of these models to improve organ allocation and post-LT survival predictions.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/s12911-025-02890-3","type":"journal-article","created":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T12:49:53Z","timestamp":1740401393000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Transforming liver transplant allocation with artificial intelligence and machine learning: a systematic review"],"prefix":"10.1186","volume":"25","author":[{"given":"Lisiane","family":"Pruinelli","sequence":"first","affiliation":[]},{"given":"Kiruthika","family":"Balakrishnan","sequence":"additional","affiliation":[]},{"given":"Sisi","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Zhigang","family":"Li","sequence":"additional","affiliation":[]},{"given":"Anji","family":"Wall","sequence":"additional","affiliation":[]},{"given":"Jennifer C.","family":"Lai","sequence":"additional","affiliation":[]},{"given":"Jesse D.","family":"Schold","sequence":"additional","affiliation":[]},{"given":"Timothy","family":"Pruett","sequence":"additional","affiliation":[]},{"given":"Gyorgy","family":"Simon","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,24]]},"reference":[{"key":"2890_CR1","doi-asserted-by":"crossref","unstructured":"Sendak M, Elish MC, Gao M, Futoma J, Ratliff W, Nichols M et al. 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