Artificial Intelligence and Digital Biomarkers in Hepatology: Critical Perspectives, Emerging Evidence, and Future Directions
Abstract
Liver diseases are a major global health burden, responsible for nearly two million deaths worldwide each year. Despite advances in imaging, serology, and non-invasive fibrosis assessment, late-stage diagnosis persists, limiting curative interventions. Artificial intelligence (AI) and digital biomarkers promise to transform hepatology by enhancing early detection, risk stratification, and remote monitoring. This review provides a critical synthesis of recent evidence in AI-driven imaging, digital histopathology, predictive modeling using electronic health records (EHR), and wearable-based phenotyping. We compare and analyze the strengths and limitations of landmark AI models, highlight real-world implementation barriers such as algorithmic bias and data privacy, and explore emerging paradigms such as federated learning and multimodal integration. While AI tools consistently outperform conventional scores (e.g., Model for End-Stage Liver Disease [MELD]) in predictive accuracy, their clinical adoption remains limited by regulatory, ethical, and validation challenges. In the future, hepatology will require equitable AI systems trained on diverse datasets, integration into electronic medical record (EMR) workflows, and patient-centered digital health platforms. Establishing global AI liver disease registries and multicenter validation trials will be critical to ensure equitable and scalable clinical adoption.
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