Ge, William H.

Artificial intelligence (AI) has emerged as a transformative force in liver transplantation (LT), spanning patient selection, donor–recipient matching, intraoperative management, and post-transplant care. Historically, applications have relied on traditional machine learning (ML) and deep learning (DL) techniques trained on structured clinical datasets to improve risk stratification and outcome prediction. Since 2023, however, advances in generative AI (GenAI) and large language models (LLMs) have expanded the scope of AI in LT, enabling multimodal reasoning and the extraction of clinically meaningful information from unstructured data. This review synthesizes recent AI-driven developments across the LT patient journey, from advanced chronic liver disease management to long-term post-transplant outcomes. Representative applications include ML-based models to optimize treatment selection and waitlist prioritization for hepatocellular carcinoma, random forest and gradient-boosting approaches to improve donor utilization and predict graft survival, and DL-based imaging models for graft volumetry and steatosis assessment. Post-transplant innovations encompass neural network models for graft injury classification, recurrent architectures for immunosuppression dosing and fibrosis prediction, multi-task frameworks for complication risk stratification, and integrative omics-based approaches to detect rejection and dysfunction. Emerging GenAI applications include LLM-enabled digital phenotyping of social determinants of health and multi-agent simulations of transplant selection committees. Despite rapid progress, translation into routine practice remains limited by challenges in local validation, workflow integration, and infrastructure requirements. Future opportunities lie in incorporating underutilized data domains, such as psychosocial factors, -omics, and intraoperative physiologic streams, and leveraging GenAI to enhance clinical decision support, research scalability, and patient–clinician communication. AI is poised not only to refine prediction but to reshape the conceptual and operational landscape of liver transplantation.

Abstract Defective microRNA (miRNA) biogenesis contributes to the development and progression of epithelial ovarian cancer (EOC). In this study, we examined the hypothesis that single nucleotide polymorphisms (SNP) in miRNA biogenesis genes may influence EOC risk. In an initial investigation, 318 SNPs in 18 genes were evaluated among 1,815 EOC cases and 1,900 controls, followed up by a replicative joint meta-analysis of data from an additional 2,172 cases and 3,052 controls. Of 23 SNPs from 9 genes associated with risk (empirical P < 0.05) in the initial investigation, the meta-analysis replicated 6 SNPs from the DROSHA, FMR1, LIN28, and LIN28B genes, including rs12194974 (G>A), an SNP in a putative transcription factor binding site in the LIN28B promoter region (summary OR = 0.90, 95% CI: 0.82–0.98; P = 0.015) which has been recently implicated in age of menarche and other phenotypes. Consistent with reports that LIN28B overexpression in EOC contributes to tumorigenesis by repressing tumor suppressor let-7 expression, we provide data from luciferase reporter assays and quantitative RT-PCR to suggest that the inverse association among rs12194974 A allele carriers may be because of reduced LIN28B expression. Our findings suggest that variants in LIN28B and possibly other miRNA biogenesis genes may influence EOC susceptibility. Cancer Res; 71(11); 3896–903. ©2011 AACR.