Abstract:Medical digital biopsy (MC-Biopsy) is a data integration and application framework developed in the context of the growing convergence of artificial intelligence and large-scale clinical data, aiming to address the fragmentation and limited reusability of heterogeneous clinical information in real-world practice. Centered on standardized data governance, MC-Biopsy systematically integrates laboratory results, imaging, pathology, clinical narratives, and longitudinal follow-up data to construct a disease-specific, multimodal database covering the entire disease course, while embedding artificial intelligence-based methods into existing clinical data structures and workflows to facilitate the transformation of high-value clinical data into reusable evidence. Taking the MC-Biopsy framework preliminarily established and implemented at our institution as an example, this article describes its overall concept, core technical architecture, and real-world deployment, with a particular focus on its application in key scenarios of liver disease management, including risk stratification of high-risk populations, tumor diagnosis and staging, treatment response assessment, and prognosis evaluation. In practice, MC-Biopsy supports individualized and stratified risk management by integrating established models such as the aMAP (age-Male-ALBi-Platelets score) into outpatient and follow-up workflows and by linking longitudinal laboratory and imaging data. In addition, natural language processing-based structuring of clinical narratives contributes to improving data quality and supporting standardized clinical training. Through multimodal integration of imaging, pathology, and clinical data, a traceable digital phenotype repository is progressively established, enabling liver disease research to shift from single-modality, static indicator-based analyses toward multidimensional investigations grounded in longitudinal disease trajectories and real-world outcomes. From the perspective of discipline development, this study further discusses the potential role of MC-Biopsy in problem-oriented translational research and interdisciplinary medical education. Overall, MC-Biopsy represents a reproducible and scalable technical reference with the potential to support precision clinical care, real-world research, and the development of learning healthcare systems.