Due to persistent diagnostic delays, varied disease progression, and unpredictable therapy responses, viral hepatitis remains a major worldwide health concern despite advances in antiviral medication and vaccine. Precision hepatology is a viable strategy for overcoming these limitations through tailored, data-driven clinical care. The confluence of digital twin systems, multi-omics technologies, and artificial intelligence (AI) as an integrated ecosystem for precision hepatitis therapy is covered in detail in this research. Multi-omics systems that offer comprehensive molecular characterisation of disease heterogeneity and help identify biomarkers include genomics, transcriptomics, epigenomics, proteomics, metabolomics, microbiomics, and radiomics. AI techniques enhance prognostic assessment, fibrosis staging, hepatocellular carcinoma risk prediction, early identification, and customised treatment selection. These techniques include explainable AI, generative AI, machine learning, and deep learning. Digital twin technologies enable dynamic virtual patient modelling, therapy simulation, real-time sickness monitoring, and predictive clinical decision support. The study also highlights novel computational techniques including knowledge graphs, graph neural networks, federated learning, and multimodal data integration that facilitate translational precision hepatology. A critical analysis is conducted of present and future viewpoints regarding data standardisation, interoperability, algorithmic bias, privacy, regulatory validation, and ethical governance. The combination of AI, multi-omics, and digital twins promises a breakthrough paradigm for predictive, preventative, personalised, and participative (P4) hepatitis therapy with the potential to greatly improve clinical outcomes and accelerate precision medicine
Kalyani Chande, Rishabh P. Soni, Riddhi S Petkar, Raj A. Patil· Zenodo (CERN European Organi...· 0 citations
Due to persistent diagnostic delays, varied disease progression, and unpredictable therapy responses, viral hepatitis remains a major worldwide health concern despite advances in antiviral medication and vaccine. Precision hepatology is a viable strategy for overcoming these limitations through tailored, data-driven clinical care. The confluence of digital twin systems, multi-omics technologies, and artificial intelligence (AI) as an integrated ecosystem for precision hepatitis therapy is covered in detail in this research. Multi-omics systems that offer comprehensive molecular characterisation of disease heterogeneity and help identify biomarkers include genomics, transcriptomics, epigenomics, proteomics, metabolomics, microbiomics, and radiomics. AI techniques enhance prognostic assessment, fibrosis staging, hepatocellular carcinoma risk prediction, early identification, and customised treatment selection. These techniques include explainable AI, generative AI, machine learning, and deep learning. Digital twin technologies enable dynamic virtual patient modelling, therapy simulation, real-time sickness monitoring, and predictive clinical decision support. The study also highlights novel computational techniques including knowledge graphs, graph neural networks, federated learning, and multimodal data integration that facilitate translational precision hepatology. A critical analysis is conducted of present and future viewpoints regarding data standardisation, interoperability, algorithmic bias, privacy, regulatory validation, and ethical governance. The combination of AI, multi-omics, and digital twins promises a breakthrough paradigm for predictive, preventative, personalised, and participative (P4) hepatitis therapy with the potential to greatly improve clinical outcomes and accelerate precision medicine
Kalyani Chande, Rishabh P. Soni, Riddhi S Petkar, Raj A. Patil· Zenodo (CERN European Organi...· 0 citations
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