2022
DOI: 10.1111/aor.14334
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Machine learning and artificial intelligence in cardiac transplantation: A systematic review

Abstract: Background This review aims to systematically evaluate the currently available evidence investigating the use of artificial intelligence (AI) and machine learning (ML) in the field of cardiac transplantation. Furthermore, based on the challenges identified we aim to provide a series of recommendations and a knowledge base for future research in the field of ML and heart transplantation. Methods A systematic database search was conducted of original articles that explored the use of ML and/or AI in heart transp… Show more

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Cited by 23 publications
(13 citation statements)
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“…A large number of transplant-related tools or predictive models (especially as new technologies in AI emerge) are being created; however, the pipeline for implementing these tools into clinical practice is extremely sparse. [70][71][72][73] They are generally not implemented in a prospective manner in real patient cohorts to assess the impact of these tools on clinical outcomes. Furthermore, especially within the AI-based CDSS tools, there is sparse analysis of the explainability of these tools and individualized analysis of end-user requirements for the transparency of such clinical programs.…”
Section: Discussionmentioning
confidence: 99%
“…A large number of transplant-related tools or predictive models (especially as new technologies in AI emerge) are being created; however, the pipeline for implementing these tools into clinical practice is extremely sparse. [70][71][72][73] They are generally not implemented in a prospective manner in real patient cohorts to assess the impact of these tools on clinical outcomes. Furthermore, especially within the AI-based CDSS tools, there is sparse analysis of the explainability of these tools and individualized analysis of end-user requirements for the transparency of such clinical programs.…”
Section: Discussionmentioning
confidence: 99%
“…Although algorithms to identify B or T cell-mismatched HLA epitopes may be one of the best tools developed in the modern era, it is crucial to address the ethical governance issues surrounding their utilization, while B and T cell-mismatched HLA epitope identification algorithms offer promising medical benefits, addressing ethical governance issues is essential to protect individuals' rights, privacy, and well-being, as well as to promote fairness and responsible use of these tools in healthcare and research. [11][12][13]19 HLAMatchmaker can calculate the number of eplet mismatches present in the donor but not in the recipient. The newly developed Predicted Indirectly Recognizable Epitope tool 14 may be useful for this purpose; however, it is not applicable for individual patients.…”
Section: Challengesmentioning
confidence: 99%
“…There are new artificial intelligence-based algorithms to help with patient selection, with the final decision still resting with the transplant cardiologist. 54 This may help with the multiple factors that go into donor: recipient matching.…”
Section: What Can We Learn From This About Treatment?mentioning
confidence: 99%