{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T18:36:11Z","timestamp":1792866971243,"version":"4.5.0"},"reference-count":27,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T00:00:00Z","timestamp":1792540800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["J Oncol Pharm Pract"],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To critically review current evidence on artificial intelligence (AI) in breast cancer pharmacotherapy, emphasizing treatment optimization, treatment-response prediction, toxicity, medication safety, pharmacovigilance, clinical decision support, and the evolving role of oncology pharmacists.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Data Sources<\/jats:title>\n                    <jats:p>A structured narrative search used PubMed\/MEDLINE as the principal database, supplemented by Google Scholar, citation tracking, and reference-list screening. The final PubMed search was executed on September 20, 2026, using a publication cutoff of September 14, 2026, corresponding to the date of the editorial decision, and identified 764 records. The six-day interval reflects the timing of the search rerun after the publication cutoff; publications after September 14 were not included. Evidence relevant to AI-supported breast cancer pharmacotherapy and oncology pharmacy practice was prioritized.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Data Summary<\/jats:title>\n                    <jats:p>AI applications include treatment-response prediction, clinical decision support, treatment stratification, toxicity prediction, and pharmacovigilance. Machine-learning models have addressed severe treatment-related toxicity, febrile neutropenia, and cardiotoxicity, while AI-based pharmacovigilance may support detection of adverse-event and polypharmacy patterns. However, much of the evidence remains retrospective or focused on technical performance rather than demonstrated improvement in patient-centered outcomes. Direct prospective evidence evaluating AI-integrated oncology pharmacist interventions remains limited.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>AI applications in breast cancer pharmacotherapy show promising technical and, in selected settings, clinical evidence, but evidence maturity varies substantially across applications. Direct prospective evidence demonstrating additional clinical benefit from integrated AI\u2013oncology pharmacist interventions remains limited. Pharmacist-in-the-loop approaches should therefore be regarded as conceptual and testable models rather than established standards of care.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/10781552261498674","type":"journal-article","created":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T03:42:03Z","timestamp":1792640523000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence in breast cancer pharmacotherapy: Treatment optimization, medication safety, and the evolving role of oncology pharmacists"],"prefix":"10.1177","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-2294-8766","authenticated-orcid":false,"given":"Anhar Adel Abdulla","family":"Abuzaid","sequence":"first","affiliation":[{"name":"National University \u2013 Sudan","place":["Sudan"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,10,21]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1200\/EDBK_432442"},{"key":"e_1_3_3_3_2","article-title":"Global cancer statistics 2024: GLOBOCAN estimates of incidence and mortality worldwide for 34 cancers in 186 countries","volume":"76","author":"Sung H","year":"2026","unstructured":"Sung H, Filho AM, Laversanne M, et al. Global cancer statistics 2024: GLOBOCAN estimates of incidence and mortality worldwide for 34 cancers in 186 countries. CA Cancer J Clin 2026; 76: e70090.","journal-title":"CA Cancer J Clin"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.breast.2022.08.010"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00432-023-04630-4"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.3390\/curroncol31080312"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1177\/1078155220988333"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2022.104983"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1177\/20552076251388145"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ctrv.2022.102410"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1038\/s43856-025-01342-3"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1186\/s41073-019-0064-8"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-025-66384-6"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.1038\/s43018-024-00756-7"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1200\/CCI.20.00018"},{"key":"e_1_3_3_16_2","first-page":"413","article-title":"Consistency of CSCO AI with multidisciplinary clinical decision-making teams in breast cancer: a retrospective study","volume":"16","author":"Xu W","year":"2024","unstructured":"Xu W, Wang X, Yang L, et al. Consistency of CSCO AI with multidisciplinary clinical decision-making teams in breast cancer: a retrospective study. Breast Cancer (Dove Med Press) 2024; 16: 413\u2013422.","journal-title":"Breast Cancer (Dove Med Press)"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ccell.2025.11.003"},{"key":"e_1_3_3_18_2","article-title":"Machine learning to predict the individual risk of treatment-relevant toxicity for patients with breast cancer undergoing neoadjuvant systemic treatment","volume":"8","author":"Cai L","year":"2024","unstructured":"Cai L, Deutsch TM, Sidey-Gibbons C, et al. Machine learning to predict the individual risk of treatment-relevant toxicity for patients with breast cancer undergoing neoadjuvant systemic treatment. JCO Clin Cancer Inform 2024; 8: e2400010.","journal-title":"JCO Clin Cancer Inform"},{"key":"e_1_3_3_19_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-71927-6"},{"key":"e_1_3_3_20_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00204-022-03341-y"},{"key":"e_1_3_3_21_2","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCOUTCOMES.124.011504"},{"key":"e_1_3_3_22_2","doi-asserted-by":"publisher","DOI":"10.1097\/JS9.0000000000003538"},{"key":"e_1_3_3_23_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13058-025-02013-w"},{"key":"e_1_3_3_24_2","doi-asserted-by":"publisher","DOI":"10.3390\/pharmacy13060176"},{"key":"e_1_3_3_25_2","volume-title":"FDA Adverse Event Monitoring System (AEMS): public dashboard and data limitations","author":"U.S. Food and Drug Administration","year":"2026","unstructured":"U.S. Food and Drug Administration. FDA Adverse Event Monitoring System (AEMS): public dashboard and data limitations. U.S. Food and Drug Administration (FDA), 2026. Updated 2026. Accessed August 9, 2026. Available from: https:\/\/www.fda.gov\/safety\/fda-adverse-event-monitoring-system-aems"},{"key":"e_1_3_3_26_2","doi-asserted-by":"publisher","DOI":"10.6028\/NIST.AI.100-1"},{"key":"e_1_3_3_27_2","isbn-type":"print","volume-title":"Ethics and governance of artificial intelligence for health: WHO guidance","author":"World Health Organization","year":"2021","unstructured":"World Health Organization. Ethics and governance of artificial intelligence for health: WHO guidance. Geneva: World Health Organization; 2021. ISBN: 9789240029200.","ISBN":"https:\/\/id.crossref.org\/isbn\/9789240029200"},{"key":"e_1_3_3_28_2","volume-title":"Guiding principles of good AI practice in drug development","author":"U.S. Food and Drug Administration","year":"2026","unstructured":"U.S. Food and Drug Administration. European Medicines Agency. Guiding principles of good AI practice in drug development. U.S. Food and Drug Administration (FDA) and European Medicines Agency (EMA), 2026. Published January 14, 2026. Accessed August 9, 2026. Available from: https:\/\/www.ema.europa.eu\/en\/about-us\/how-we-work\/data-regulation-big-data-other-sources\/artificial-intelligence"}],"container-title":["Journal of Oncology Pharmacy Practice"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/10781552261498674","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/10781552261498674","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/10781552261498674","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T17:50:02Z","timestamp":1792864202000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/10781552261498674"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,21]]},"references-count":27,"alternative-id":["10.1177\/10781552261498674"],"URL":"https:\/\/doi.org\/10.1177\/10781552261498674","relation":{},"ISSN":["1078-1552","1477-092X"],"issn-type":[{"value":"1078-1552","type":"print"},{"value":"1477-092X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,21]]},"article-number":"10781552261498674"}}