AI and Herbal Therapeutics in Hepatitis B

Chronic hepatitis B virus infection is still a major global health problem, with an estimated 240 million people living with the disease in 2024. Yet less than 5% are currently receiving treatment. Persistent gaps in early diagnosis and long-term treatment access, especially in the WHO African and Western Pacific regions, hinder progress towards the WHO 2030 elimination target. This review comprises two distinct but emerging intersecting approaches to addressing these gaps: artificial intelligence and herbal medicine. AI models, which include imaging-based fibrosis staging and laboratory-based machine learning tools, enable earlier non-invasive diagnosis of liver fibrosis in HBV patients. Though most models to date have been geographically concentrated predominantly in China, herbal therapeutics, most notably genus Phyllanthus, have acquired over two decades of randomized clinical trial evidence, which demonstrate antiviral activity and improved viral marker clearance. In retrospect, long-term clinical outcome data is absent. Furthermore, it explores the emerging convergence between the fields in which network pharmacology and computational target prediction methods are applied to hepatitis-related herbal formula to identify specific bioactive compounds and mechanisms with precision that traditional whole extract trials cannot achieve. While this integration holds genuine translational promise, major gaps remain, which include limited clinical validation of AI-predicted herbal mechanisms, inconsistent standardization practices, and less geographic diversity in existing research. Narrowing these gaps through long-term planning of clinical trials, collaboration, and clearer regulatory pathways is essential to realizing the full potential of this convergence in supporting global hepatitis B elimination efforts.