{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T16:41:07Z","timestamp":1791650467864,"version":"4.3.4"},"reference-count":39,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T00:00:00Z","timestamp":1763337600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T00:00:00Z","timestamp":1763337600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Artificial Organs"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>AI holds significant promise for guiding clinical decisions in end\u2010stage organ failure, where treatment options now include medical management, transplantation, mechanical support devices, and palliative care. This paper discusses current applications of AI in healthcare, emphasizing the complex decision\u2010making necessary for patients with organ failure. It outlines how AI can support risk stratification, patient selection, and outcome prediction, particularly in transplantation practices that increasingly rely on robust data to inform care pathways. By analyzing large datasets from electronic health records, imaging, and patient\u2010reported outcomes, AI can help physicians forecast long\u2010term survival and quality of life, and potentially assist clinicians in modifying treatment strategies before adverse trajectories take hold. There is a need for standardized, high\u2010quality data, rigorous validation, and transparent algorithms to mitigate biases that could exacerbate disparities in care. Ethical considerations demand attention to equitable access, patient privacy, and the preservation of the human element in patient\u2010clinician relationships. Patients generally view AI with cautious optimism, recognizing its potential to augment care but also voicing concerns about data protection and the risk of losing compassionate, personal care. Importantly, AI can help with \u201calignment,\u201d or fitting treatment recommendations to patients' values, and promote sustainable and patient\u2010centered outcomes. Ultimately, the successful integration of AI into daily practice requires multidisciplinary collaboration among developers, clinicians, ethicists, and regulators. Deep stakeholder engagement, continuous algorithmic refinement, and user\u2010friendly design are pivotal to ensuring that AI serves as a practical decision\u2010support tool that complements, rather than replaces, clinical expertise and shared decision\u2010making.<\/jats:p>","DOI":"10.1111\/aor.70027","type":"journal-article","created":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T09:57:23Z","timestamp":1763373443000},"page":"173-179","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["<scp>AI<\/scp>\n                    \u2010Assisted Decision\u2010Making for End\u2010Stage Organ Failure: Opportunities and Ethical Concerns"],"prefix":"10.1111","volume":"50","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5970-8324","authenticated-orcid":false,"given":"John W.","family":"Haller","sequence":"first","affiliation":[{"name":"Advanced Technologies and Surgery Branch, Division of Cardiovascular Sciences National Heart, Lung, and Blood Institute, National Institutes of Health (NIH)  Bethesda Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olga D.","family":"Brazhnik","sequence":"additional","affiliation":[{"name":"Advanced Technologies and Surgery Branch, Division of Cardiovascular Sciences National Heart, Lung, and Blood Institute, National Institutes of Health (NIH)  Bethesda Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6224-0699","authenticated-orcid":false,"given":"Kathleen N.","family":"Fenton","sequence":"additional","affiliation":[{"name":"Advanced Technologies and Surgery Branch, Division of Cardiovascular Sciences National Heart, Lung, and Blood Institute, National Institutes of Health (NIH)  Bethesda Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,11,17]]},"reference":[{"key":"e_1_2_15_2_1","doi-asserted-by":"publisher","DOI":"10.1159\/000514550"},{"key":"e_1_2_15_3_1","doi-asserted-by":"publisher","DOI":"10.1093\/cvr\/cvac013"},{"key":"e_1_2_15_4_1","doi-asserted-by":"publisher","DOI":"10.1136\/gutjnl-2020-322161"},{"key":"e_1_2_15_5_1","doi-asserted-by":"publisher","DOI":"10.5578\/tt.69753"},{"key":"e_1_2_15_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/ejhf.1783"},{"key":"e_1_2_15_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s43681-023-00297-2"},{"key":"e_1_2_15_8_1","doi-asserted-by":"publisher","DOI":"10.1093\/ehjdh\/ztad001"},{"key":"e_1_2_15_9_1","doi-asserted-by":"publisher","DOI":"10.1097\/TP.0000000000005023"},{"key":"e_1_2_15_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/adtp.202300236"},{"key":"e_1_2_15_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhep.2023.01.006"},{"key":"e_1_2_15_12_1","article-title":"Artificial Intelligence and Robotics in Transplant Surgery: Advancements and Future Directions","volume":"15","author":"Bokhari S. 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