From prediction to practice: a narrative review of recent artificial intelligence applications in liver transplantation

Abstract

Liver transplantation (LT) is the definitive treatment for end-stage liver disease and certain liver cancers. This involves complex decision making across the transplant continuum. Artificial intelligence (AI), with its ability to analyze high-dimensional data and derive meaningful patterns, shows promise as a transformative tool to address these challenges. In this narrative review, we searched PubMed from January 2021 to October 2024 using keywords such as “artificial intelligence”, “machine learning”, “deep learning”, and “liver transplantation”. Only full-text, English-language studies on adult populations (with minimum sample sizes deemed appropriate by each study’s design) were included, with a total of 65 articles. These publications examined AI applications in pre-transplant risk assessment (9), donor liver assessment (11), transplant oncology (11), graft survival prediction (7), overall survival prediction (11), immunosuppression management (4), and post-transplant risk prediction (12). Tree-based methods showed high accuracy in predictive tasks, while deep learning excelled in medical imaging analysis. Despite these advancements, only 6% of studies addressed algorithmic fairness, and 41% of neural network implementations lacked interpretability methods. Key challenges included data harmonization, multicenter validation, and integration with existing clinical workflows. Despite these limitations, AI continues to show promise for optimizing critical steps along the LT continuum. As the field progresses, the focus must remain on using AI to expand access and optimize care, ensuring it supports rather than restricts transplant opportunities.