ARTIFICIAL INTELLIGENCE IN VIRAL HEPATITIS: ADVANCING EARLY DETECTION, PERSONALIZED TREATMENT, AND GLOBAL ELIMINATION STRATEGIES

Abstract

Viral hepatitis remains a major global cause of chronic liver disease, cirrhosis, hepatocellular carcinoma, and liver-related mortality despite advances in vaccination, antiviral therapy, and public health interventions. Delayed diagnosis, asymptomatic disease progression, limited healthcare access, and unequal resource distribution continue to challenge the World Health Organization's goal of eliminating viral hepatitis by 2030. Artificial intelligence (AI), including machine learning, deep learning, natural language processing, computer vision, explainable AI, and large language models, has emerged as a powerful clinical decision-support tool capable of improving viral hepatitis management through data-driven analysis. This review highlights the expanding role of AI in early disease detection, laboratory and molecular diagnosis, risk prediction, disease surveillance, medical imaging, fibrosis staging, cirrhosis detection, patient stratification, treatment response prediction, precision medicine, and individualized antiviral therapy. It also discusses AI-driven advances in drug discovery, drug repurposing, virtual screening, protein structure prediction, clinical trial optimization, and radiomics, which have enhanced non-invasive assessment of liver disease and personalized patient care. Although challenges such as limited multicenter validation, data heterogeneity, algorithm transparency, ethical and regulatory concerns, data privacy, and clinical integration remain, continued multidisciplinary collaboration will facilitate the development of robust and explainable AI systems. When responsibly integrated into evidence-based clinical practice, AI has the potential to improve diagnostic accuracy, optimize therapeutic decision-making, advance precision hepatology, and accelerate progress toward the global elimination of viral hepatitis.