Revolutionizing viral hepatitis management: Artificial intelligence-assisted diagnosis and personalized treatment

This is the authors' abstract. We don't add key points for this paper.

Viral hepatitis, including hepatitis B and hepatitis C (HCV), remains a significant global health burden, leading to liver fibrosis, cirrhosis, and hepatocellular carcinoma. Traditional diagnostic methods, while effective, often face limitations in accuracy, accessibility, and timeliness. Artificial intelligence (AI) has emerged as a transformative tool in healthcare, enhancing the detection, diagnosis, and treatment of viral hepatitis. This review explores the role of AI in viral hepatitis management, focusing on early detection through image analysis, digital pathology, and machine learning algorithms. AI-driven image analysis tools, such as convolutional neural networks, have demonstrated high accuracy in detecting HCV-related liver lesions from computed tomography scans. Supervised learning models such as support vector machines and hybrid quantum neural networks further enhance early risk stratification. AI also facilitates personalized treatment by predicting treatment responses, accelerating drug discovery, and advancing precision medicine. Furthermore, AI contributes to epidemiological surveillance by predicting disease spread and tracking treatment adherence. Despite its potential, challenges such as data privacy, algorithmic bias, and regulatory compliance must be addressed to ensure equitable and effective AI implementation. Future directions include integrating AI into clinical workflows and expanding AI applications in low-resource settings. AI-assisted diagnosis and management have the potential to revolutionize viral hepatitis care, improving patient outcomes and reducing the global disease burden.