MULTIMODAL DIGITAL BIOMARKERS IN ONCOLOGY: ARTIFICIAL INTELLIGENCE FOR EARLY DETECTION AND PERSONALIZED TREATMENT

Abstract

Digital biomarkers have emerged as one of the most transformative innovations in precision oncology by enabling continuous, objective, and quantitative assessment of cancer biology through integration of multimodal biomedical data. Unlike conventional biomarkers that often rely on isolated molecular or laboratory measurements, multimodal digital biomarkers combine radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, wearable physiological monitoring, laboratory investigations, electronic health records, patient-reported outcomes, and real-world clinical data into comprehensive computational representations of disease. Recent advances in artificial intelligence (AI), particularly foundation models, multimodal transformers, graph neural networks, self-supervised learning, and generative AI, have fundamentally transformed digital biomarker discovery by enabling generalized biomedical representation learning across heterogeneous data modalities. These intelligent systems facilitate early cancer detection, molecular characterization, prognostic prediction, therapeutic response assessment, immunotherapy optimization, adaptive disease monitoring, digital twin simulation, and personalized clinical decision support. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen multimodal biomarker ecosystems by enabling collaborative, privacy-preserving, and continuously adaptive computational oncology. Despite remarkable technological progress, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, interpretability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of multimodal digital biomarkers in oncology, emphasizing artificial intelligence as the driving force for early cancer detection and personalized treatment.