Artificial Intelligence, Digital Biomarkers, and Precision Oncology: Emerging Technologies for Early Detection and Personalized Intervention

Abstract

Cancer remains a leading cause of global morbidity and mortality, highlighting the urgent need for earlier diagnosis, accurate risk stratification, and individualized therapeutic strategies. Advances in artificial intelligence (AI) and digital biomarkers have transformed oncology by enabling the integration of molecular, imaging, and clinical data into comprehensive computational frameworks for precision cancer care. Digital biomarkers—including circulating tumor DNA (ctDNA), circulating tumor cells, radiomic signatures, computational pathology, wearable sensor data, and multi-omics profiles—provide dynamic and minimally invasive measures of tumor biology throughout the disease continuum. Simultaneously, deep learning, transformer architectures, foundation models, and multimodal AI have substantially improved the extraction of clinically meaningful information from heterogeneous biomedical datasets. Recent studies demonstrate that AI-assisted liquid biopsy, radiomics, and computational pathology significantly enhance early cancer detection, prognostic assessment, treatment selection, and longitudinal monitoring compared with conventional diagnostic approaches. Emerging technologies such as federated learning, explainable AI, digital twins, and autonomous clinical decision-support systems further facilitate secure, interpretable, and personalized oncology. Nevertheless, widespread clinical implementation remains limited by challenges related to data heterogeneity, algorithmic bias, model interpretability, regulatory approval, interoperability, and prospective clinical validation. This review provides a comprehensive overview of AI-enabled digital biomarkers across the oncology continuum, discusses recent advances in multimodal data integration and computational intelligence, and examines future directions toward adaptive, patient-centered precision oncology. Continued collaboration among clinicians, computational scientists, regulatory agencies, and healthcare systems will be essential to translate these technologies into routine clinical practice while ensuring safety, equity, and clinical effectiveness.