Transformasi Biomedik Menuju Personalisasi dan Kesehatan Presisi pada Kanker

Abstract

Biomedical transformation is redefining cancer research and clinical practice by integrating experimental and computational approaches, digital technologies, and patient-specific models to realize precision health. Wet lab experiments generate molecular, imaging, and clinical data, while computational biology and artificial intelligence (AI) interpret complex datasets to uncover predictive biomarkers, optimize therapy selection, and simulate tumor behavior. Digital transformation accelerates data integration through multi-omics, radiomics, and electronic health records, enabling the development of multimodal biomarkers and improved patient stratification. Furthermore, patient-specific models, such as patient-derived organoids (PDOs), xenografts, and digital twin systems preserve tumor heterogeneity and microenvironmental complexity, allowing individualized drug testing and treatment prediction. The convergence of these frameworks’ advances precision oncology from a population-based paradigm toward personalized, adaptive, and data-driven cancer care. This integrative approach strengthens translational research, enhances diagnostic accuracy, and promotes more effective, patient-centered therapeutic strategies.