Artificial Intelligence in the Early Detection and Elimination of Viral Hepatitis: Current Advances, Challenges, and Future Perspectives.

Abstract

Viral hepatitis remains a formidable global health threat, contributing to approximately 1.3 million deaths annually since 2022. Despite the World Health Organization’s (WHO) 2030 elimination targets aiming for a 90% reduction in new infections and a 65% reduction in mortality current diagnostic and treatment coverage remains critically low. This article explores the transformative role of Artificial Intelligence (AI), including machine learning (ML), deep learning (DL), and natural language processing (NLP), in revolutionizing the management of viral hepatitis. Current advances include the use of convolutional neural networks (CNNs) for non-invasive imaging, NLP for mining electronic health records (EHR) to identify high-risk individuals, and predictive analytics for disease progression and treatment adherence. Furthermore, the integration of computational models with traditional medicine, specifically the use of herbal compounds like Shyonaka (Oroxylum indicum) in managing chronic hepatitis B, presents a novel frontier in integrative hepatology. Despite these advances, significant challenges persist, including algorithmic bias, data privacy concerns, and regulatory hurdles in clinical translation. This review synthesizes current evidence to provide a roadmap for a multi-tiered, risk-stratified AI framework for the global elimination of viral hepatitis.