{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:12:06Z","timestamp":1787026326592,"version":"build-2736575974"},"reference-count":56,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,2,24]],"date-time":"2023-02-24T00:00:00Z","timestamp":1677196800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cardiovasc. Med."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Artificial intelligence can recognize complex patterns in large datasets. It is a promising technology to advance heart failure practice, as many decisions rely on expert opinions in the absence of high-quality data-driven evidence.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We searched Embase, Web of Science, and PubMed databases for articles containing \u201cartificial intelligence,\u201d \u201cmachine learning,\u201d or \u201cdeep learning\u201d and any of the phrases \u201cheart transplantation,\u201d \u201cventricular assist device,\u201d or \u201ccardiogenic shock\u201d from inception until August 2022. We only included original research addressing post heart transplantation (HTx) or mechanical circulatory support (MCS) clinical care. Review and data extraction were performed in accordance with PRISMA-Scr guidelines.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Of 584 unique publications detected, 31 met the inclusion criteria. The majority focused on outcome prediction post HTx (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200913) and post durable MCS (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20097), as well as post HTx and MCS management (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20097,\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20093, respectively). One study addressed temporary mechanical circulatory support. Most studies advocated for rapid integration of AI into clinical practice, acknowledging potential improvements in management guidance and reliability of outcomes prediction. There was a notable paucity of external data validation and integration of multiple data modalities.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our review showed mounting innovation in AI application in management of MCS and HTx, with the largest evidence showing improved mortality outcome prediction.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fcvm.2023.1127716","type":"journal-article","created":{"date-parts":[[2023,2,24]],"date-time":"2023-02-24T02:59:26Z","timestamp":1677207566000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["Artificial intelligence guidance of advanced heart failure therapies: A systematic scoping review"],"prefix":"10.3389","volume":"10","author":[{"given":"Mohammad 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