Artificial Intelligence Frameworks in the Prevention and Early Diagnosis of Hepatitis B and C: A Comprehensive Clinical and Computational Review

Abstract

Despite major medical breakthroughs, Hepatitis B (HBV) and Hepatitis C (HCV) continue to silent-kill millions globally, primarily because they go undetected until severe liver damage sets in. While direct-acting antivirals and suppressive therapies work wonders, our screening methods are lagging behind. We still rely on patients showing risk factors or doctors actively remembering to test them. This review explores how artificial intelligence (AI) and machine learning (ML) are stepping in to change this passive approach. By integrating smart algorithms directly into hospital Electronic Medical Records (EMRs), we can now flag high-risk, asymptomatic carriers months before a human clinician might notice the signs. Furthermore, deep learning models and gradient-boosted trees are completely replacing the need for painful, invasive liver biopsies by calculating dynamic fibrosis scores from routine blood work. This paper outlines these advancements, looks closely at the algorithms making them happen (like XGBoost, Random Forests, and CNNs), and addresses the crucial next steps: making these models transparent to doctors and keeping patient data secure through federated learning.