EDGE ARTIFICIAL INTELLIGENCE IN ONCOLOGY: REAL-TIME CANCER DIAGNOSTICS AND INTELLIGENT CLINICAL MONITORING

Abstract

Edge artificial intelligence (Edge AI) is emerging as a transformative technology in precision oncology by enabling real-time processing of biomedical data directly at or near the point of care, thereby reducing latency, enhancing data privacy, and supporting intelligent clinical monitoring throughout the cancer care continuum. Unlike conventional cloud-dependent artificial intelligence systems, Edge AI integrates computational intelligence within medical imaging devices, digital pathology platforms, wearable biosensors, mobile health technologies, and Internet of Medical Things (IoMT) infrastructure to provide immediate clinical insights without requiring continuous remote computation. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, reinforcement learning, and generative artificial intelligence have enabled comprehensive integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes into intelligent edge computing ecosystems. These systems support early cancer detection, biomarker discovery, molecular characterization, prognostic prediction, therapeutic optimization, immunotherapy selection, adaptive disease monitoring, digital twin simulation, and evidence-based clinical decision support. Emerging technologies including multimodal large language models, federated learning, explainable artificial intelligence, agentic AI, cloud-edge hybrid architectures, and 6G-enabled healthcare networks further strengthen Edge AI by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical intelligence. Despite remarkable technological progress, important scientific, technical, ethical, and regulatory challenges remain regarding computational resource constraints, multimodal data harmonization, interoperability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of Edge AI in oncology, emphasizing real-time cancer diagnostics and intelligent clinical monitoring as transformative technologies for next-generation precision cancer care.