This article provides a comprehensive overview of the clinical and laboratory diagnostics of viral hepatitis types (A, B, C, D, E) in children, methods for evaluating viral load, and the potential of artificial intelligence (AI) models for prognosis. It discusses the use of machine learning algorithms like LSTM, GRU, and random forest for analyzing, forecasting, and classifying hepatitis dynamics based on viral load data obtained through serological and molecular testing. Key aspects such as data preparation, platforms, and clinical integration of AI models are also considered.

Viral hepatitis, children, artificial intelligence

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