Artificial Intelligence–Integrated Cancer Ecosystems: Multimodal Digital Biomarkers for Precision, Personalized, and Preventive Oncology

Abstract

Artificial intelligence (AI) is reshaping precision oncology by integrating multimodal digital biomarkers—including radiomics, pathomics, genomics, transcriptomics, and liquid biopsy—into unified computational ecosystems that support accurate cancer diagnosis, prognostic stratification, treatment selection, and longitudinal disease monitoring. Recent advances in transformer-based deep learning, vision-language models, self-supervised learning, and foundation models have enabled AI systems to achieve diagnostic performance approaching that of expert radiologists and pathologists while uncovering complex biological patterns beyond conventional analytical methods. Multimodal data fusion further enhances early cancer detection through the simultaneous interpretation of imaging phenotypes, molecular alterations, histopathological features, and clinical variables, facilitating personalized therapeutic decision-making and dynamic digital twin–based disease modeling. Despite these advances, widespread clinical implementation remains constrained by challenges related to model interpretability, algorithmic bias, data privacy, regulatory approval, interoperability, and integration into existing clinical workflows. This review provides a comprehensive overview of AI-integrated cancer ecosystems, highlighting recent developments in deep learning architectures, multimodal digital biomarkers, explainable AI, federated learning, and foundation models. We examine their emerging clinical applications in cancer screening, diagnosis, prognosis, treatment optimization, and response monitoring while discussing ethical, technical, and regulatory considerations for responsible implementation. Current evidence indicates that AI-enabled radiomics and computational pathology are approaching routine clinical deployment as companion diagnostic technologies, whereas privacy-preserving federated learning and multimodal foundation models are expected to accelerate large-scale validation across diverse healthcare systems. Collectively, AI-integrated cancer ecosystems represent a transformative paradigm for precision and preventive oncology, with the potential to improve diagnostic accuracy, personalize cancer management, expand equitable access to advanced analytics, and ultimately enhance patient outcomes worldwide.