{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T01:41:24Z","timestamp":1771983684580,"version":"3.50.1"},"reference-count":483,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2025,11,29]],"date-time":"2025-11-29T00:00:00Z","timestamp":1764374400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Foundation for Science and Technology","doi-asserted-by":"publisher","award":["UIDB\/50016\/2020"],"award-info":[{"award-number":["UIDB\/50016\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background: Patient blood management (PBM) is a patient-centered, evidence-based approach for optimizing anemia management, minimizing blood loss, and ensuring appropriate transfusion. Artificial intelligence (AI) provides powerful tools for prediction, diagnosis, and decision support across PBM, but current evidence remains emerging and not yet consolidated. Objectives: This review synthesizes AI applications in PBM, summarizing predictive, diagnostic, and decision support models; highlighting methodological trends; and discussing challenges for clinical translation. Methods: PubMed, Scopus, and Web of Science were searched from inception to 31 March 2025. Eligible studies reported AI models addressing the three established PBM pillars. Studies on transfusion safety and blood bank operations relevant to PBM were also included. Extracted data covered study characteristics, predictors, models, validation strategies, and performance. The findings were narratively synthesized given study heterogeneity. Results: A total of 338 studies were included, spanning anemia detection, bleeding risk stratification, transfusion prediction, transfusion safety, and inventory management. Deep learning (DL) predominated in image-based anemia detection, while ensemble and gradient boosting methods frequently outperformed baselines in bleeding and transfusion risk prediction. Recurrent and hybrid architectures proved effective for blood supply forecasting. Across domains, machine learning and DL models generally surpassed logistic regression, clinical scores, and expert judgment. Despite strong internal performance, external validation and clinical deployment remain limited. Conclusions: AI is advancing PBM by enabling earlier anemia detection, more accurate bleeding and transfusion prediction, and smarter resource allocation. Translation into practice requires standardized reporting, robust external validation, explainability, and workflow integration. Future work should emphasize multimodal learning, prospective evaluation, and cost-effectiveness.<\/jats:p>","DOI":"10.3390\/jcm14238479","type":"journal-article","created":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T16:20:29Z","timestamp":1765210829000},"page":"8479","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0909-9601","authenticated-orcid":false,"given":"Henrique","family":"Coelho","sequence":"first","affiliation":[{"name":"CBQF\u2014Centro de Biotecnologia e Qu\u00edmica Fina\u2014Laborat\u00f3rio Associado, Escola Superior de Biotecnologia, Universidade Cat\u00f3lica Portuguesa, Rua de Diogo Botelho 1327, 4169-005 Porto, Portugal"},{"name":"Servi\u00e7o de Hematologia, Unidade Local de Sa\u00fade de Vila Nova Gaia e Espinho, Rua Concei\u00e7\u00e3o Fernandes S\/N, 4434-502 Vila Nova de Gaia, Portugal"},{"name":"Departamento de Ci\u00eancias M\u00e9dicas, Campus Universit\u00e1rio de Santiago, Universidade de Aveiro, Agra do Castro, Edif\u00edcio 30, 3810-193 Aveiro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7818-5661","authenticated-orcid":false,"given":"Fernando","family":"Silva","sequence":"additional","affiliation":[{"name":"Departamento de Ci\u00eancias M\u00e9dicas, Campus Universit\u00e1rio de Santiago, Universidade de Aveiro, Agra do Castro, Edif\u00edcio 30, 3810-193 Aveiro, Portugal"},{"name":"Servi\u00e7o de Hematologia, Unidade Local de Sa\u00fade da Regi\u00e3o de Aveiro, Avenida Artur Ravara, 3814-501 Aveiro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5040-735X","authenticated-orcid":false,"given":"Marta","family":"Correia","sequence":"additional","affiliation":[{"name":"CBQF\u2014Centro de Biotecnologia e Qu\u00edmica Fina\u2014Laborat\u00f3rio Associado, Escola Superior de Biotecnologia, Universidade Cat\u00f3lica Portuguesa, Rua de Diogo Botelho 1327, 4169-005 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5381-6615","authenticated-orcid":false,"given":"Pedro Miguel","family":"Rodrigues","sequence":"additional","affiliation":[{"name":"CBQF\u2014Centro de Biotecnologia e Qu\u00edmica Fina\u2014Laborat\u00f3rio Associado, Escola Superior de Biotecnologia, Universidade Cat\u00f3lica Portuguesa, Rua de Diogo Botelho 1327, 4169-005 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1213\/ANE.0000000000006168","article-title":"Patient blood management is a new standard of care to optimize blood health","volume":"135","author":"Goobie","year":"2022","journal-title":"Anesth. 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