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Artificial Intelligence in Post-Liver Transplantation: A Scoping Review of Comparative Model Performance.

Ileana Lulic1,2, Ivan Gornik3, Jadranka Pavicic Saric1

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|February 27, 2026
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Summary

Artificial intelligence (AI) in liver transplantation (LT) care is growing, mainly for clinical prediction. However, limited external validation and real-world use hinder readiness, necessitating standardized evaluation for improved post-transplant outcomes.

Keywords:
artificial intelligenceclinical decision supportliver transplantationpost-transplant carerisk prediction

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Area of Science:

  • Medical Informatics
  • Transplantation Medicine
  • Artificial Intelligence

Background:

  • Liver transplantation (LT) care involves complex post-operative management.
  • Artificial intelligence (AI) offers potential for optimizing patient outcomes in LT.
  • Characterizing AI applications is crucial for clinical integration.

Purpose of the Study:

  • To map and characterize AI applications in post-LT care.
  • To summarize AI model performance and identify research gaps.
  • To assess the readiness of AI tools for clinical implementation.

Main Methods:

  • Scoping review following PRISMA-ScR guidelines.
  • Comprehensive search of electronic databases up to April 2025.
  • Inclusion of primary studies on AI in post-LT care (development, validation, implementation).

Main Results:

  • 65 studies included; 52 reported primary data, predominantly clinical prediction (n=43).
  • AI applications focused on graft survival, rejection, fibrosis, recurrence, and mortality.
  • Most studies were retrospective, single-center, with common internal but rare external validation.

Conclusions:

  • AI research in post-LT care is expanding, primarily for clinical prediction.
  • Limited external validation, methodological heterogeneity, and scarce real-world implementation impede clinical readiness.
  • Standardized evaluation and prospective integration are essential to leverage AI for improved post-transplant outcomes.