The evolution and revolution of artificial intelligence in hepatology: From current applications to future paradigms

Abstract

perior performance in predicting significant fibrosis compared to traditional biomarkers. [7]These models can offer nuanced risk assessments, enabling earlier interventions and personalized management strategies.In liver transplantation, AI-driven dynamic risk assessment represents a paradigm shift in organ allocation and post-transplant care.Machine learning models show superior performance in predicting both short-term complications and long-term outcomes after liver transplantation. [8]These advancements hold the potential to optimize organ allocation, personalize post-transplant management, and improve overall transplant outcomes.AI-enabled early warning systems represent a major advancement in predicting and preventing complications. [9]Novel machine learning models show promise in ruling out high-risk varices and avoiding unnecessary endoscopies in patients with compensated cirrhosis. [10]Similarly, machine learning models demonstrate high accuracy in predicting mortality in patients with hepatic encephalopathy, outperforming clinically used models. [11]These systems analyze subtle patterns in clinical and laboratory data that may elude human observation, providing hepatologists with valuable tools for proactive patient management.In HCC management, AI is guiding treatment strategies beyond initial diagnosis.AI-based decision-making tools improve adjuvant liver-directed treatment recommendations for unresectable HCC in patients who previously underwent transarterial chemoembolization.Such tools have the potential to optimize treatment sequencing and improve overall patient outcomes. [12,13]ARE

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