Abstract
Machine learning (ML) is promising in its clinical applications, particularly in liver transplantation (LT). Transplant clinicians must synthesize different demographic, clinical, serum, and imaging reports to inform their decisions, which renders these decisions susceptible to some levels of subjectivity. Owing to the complexity of managing LT recipients, clinical decision-making in LT would benefit from using a data-driven approach powered by ML. Applications of ML are endless in LT: models can inform both the pre-transplant and post-transplant decision-making process. Several groups have attempted to improve transplant candidacy decisions and augment donor–recipient matching to reduce waiting list mortality and maximize post-LT outcomes. After LT, recipients can succumb to recurrent liver pathology, graft failure, or other complications for which ML models can assist in stratifying high-risk patients and personalizing patient care. ML models are increasingly invaluable in personalized decision-making, and they have the potential to revolutionize liver transplant medicine.
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