Artificial Intelligence in Hepatitis B: Advances in Diagnosis, Risk Stratification, Prognosis Prediction, and Clinical Decision

Abstract

Background: Hepatitis B virus (HBV) infection, which affects around 240 million people globally and causes significant morbidity and mortality through chronic hepatitis, liver cirrhosis, and hepatocellular carcinoma, is still a significant global public health concern. Optimal clinical therapy is still hampered by delayed diagnosis, poor risk stratification, uneven treatment response, and insufficient disease monitoring, even in the face of powerful antiviral medications and effective vaccinations.
Aim: This review critically evaluates the emerging role of artificial intelligence in hepatitis B management, emphasizing its applications in diagnosis, Liver Fibrosis Assessment, Risk Stratification, Prognosis Prediction, Clinical Decision Support, and Comparison with Conventional Management Strategies.
Results: Machine Learning, Deep Learning, Natural Language Processing, And Explainable AI Are Examples of Artificial Intelligence Technologies That Enhance Diagnostics, Non-Invasive Fibrosis Evaluation, Cirrhosis Prediction, Hepatocellular Cancer, And Antiviral Treatment Response. Artificial intelligence (AI) enables precision medicine and tailored patient management by combining Clinical, Laboratory, Imaging, And Electronic Health Record Data. Heterogeneous datasets, insufficient external validation, algorithmic bias, difficulties with interpretability, and ethical and legal restrictions continue to impede application.
Conclusion: AI can improve hepatitis B care by enhancing early diagnosis, fibrosis staging, risk prediction, and treatment decisions by combining clinical, lab, imaging, and follow-up data. Widespread clinical application necessitates improved validation, higher-quality data, model interpretability, and ethical standards. With careful development and integration, AI has the potential to be a reliable tool in HBV management.