The Convergence of Artificial Intelligence and Next Generation Pharmacotherapy in the Management of Viral Hepatitis

Abstract

Viral hepatitis remains a leading global health burden, accounting for over 1.3 million deaths annually and serving as a primary driver of hepatocellular carcinoma (HCC) (Balogh et al., 2016). Managing the full spectrum of hepatotropic viruses—from acute, enterically transmitted strains (HAV, HEV) to chronic, parenterally transmitted genotypes (HBV, HCV, HDV)—presents distinct diagnostic, epidemiological, and therapeutic bottlenecks (Liu et al., 2021). Traditional clinical interventions frequently rely on operator-dependent imaging, invasive biopsies, and resource-intensive, empirical drug discovery pipelines (Schinazi et al., 2010). Recently, the integration of computational intelligence has emerged as a transformative paradigm in clinical hepatology (Zheng, 2026). This review provides a comprehensive synthesis of the applications of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in restructuring the viral hepatitis landscape (Mao et al., 2026). We examine how machine learning frameworks enhance epidemiological forecasting and outbreak prediction for acute strains, while driving multi-modal clinical risk stratification and non-invasive radiomic staging of fibrosis and HCC for chronic genotypes (Clusmann et al., 2026; Dagan et al., 2024; Edeh et al., 2022; Liu et al., 2021; Wang et al., 2020). Furthermore, we highlight the role of AI-driven, structure-based virtual screenings and molecular modeling in accelerating next-generation antiviral drug discovery (Du, 2026; Strum et al., 2024). Finally, we address critical translational challenges, including model interpretability (Explainable AI) and real-world clinical implementation, providing a strategic roadmap toward the global eradication of viral hepatitis (Alizadehsani et al., 2024; Schneider et al., 2023).