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Cardiovasc. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Heart transplantation remains the definitive therapy for end-stage heart failure, yet long-term outcomes are limited by three core clinical bottlenecks in immune rejection management: imprecise preoperative donor-recipient matching, overreliance on invasive endomyocardial biopsy (EMB) for postoperative rejection surveillance, and high inter-observer variability in manual pathological diagnosis of rejection. Artificial intelligence (AI) has emerged as a promising tool to address these gaps, but the methodological quality and clinical translation readiness of supporting evidence have not been comprehensively synthesized.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This structured narrative review synthesized original research published between October 1, 2020, and October 1, 2025, identified via a targeted PubMed search and manual reference screening. Two independent reviewers performed study selection and data extraction, with discrepancies resolved by consensus. Common methodological limitations across included studies were synthesized qualitatively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 42 studies were included in the final qualitative synthesis. Preoperatively, 3D-Convolutional Neural Networks (3D-CNNs) enabled automated, accurate total cardiac volume (TCV) measurement for anatomical matching, while machine learning models identified non-linear synergistic risk factors for postoperative adverse events, outperforming traditional regression models. Postoperatively, AI models integrating non-invasive biomarkers (gene expression profiles, extracellular vesicles, donor-derived cell-free DNA) showed high diagnostic accuracy for rejection, with one single-center retrospective study estimating a 56.8% reduction in unnecessary EMB procedures (prospective clinical validation is still required). For pathological diagnosis, AI models improved the sensitivity of high-grade acute cellular rejection (ACR) detection from 39.5% to 74.4% compared with manual assessment, generative adversarial networks (GANs) addressed rare rejection sample scarcity with a rejection region detection AUROC of 98.84%, and explainable AI tools aligned model decisions with pathologists' judgment. The overall methodological quality of included studies was suboptimal, with most studies limited by single-center retrospective design, small sample size, and lack of independent external validation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>AI has demonstrated promising potential to optimize donor-recipient matching, enable non-invasive rejection surveillance, and standardize pathological diagnosis in heart transplantation. However, most current evidence comes from exploratory, single-center retrospective studies with important methodological limitations that restrict their immediate clinical translation. Future research should prioritize prospective, multi-center clinical validation, standardized biomarker and model reporting, and federated learning data ecosystems to translate AI innovations into routine clinical practice.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fcvm.2026.1790244","type":"journal-article","created":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T05:45:58Z","timestamp":1778737558000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence optimizes immune rejection prediction and management in heart transplantation: a structured narrative review"],"prefix":"10.3389","volume":"13","author":[{"given":"Kaixin","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology","place":["Wuhan, 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