Artificial Intelligence-Powered Internet of Medical Things in Radiation Oncology

Background: The Internet of Medical Things (IoMT)—a system of interconnected medical devices and sensors—offers new opportunities to enhance cancer care through real-time data collection, remote monitoring, and intelligent automation. In radiation oncology, IoMT is especially impactful given the field’s reliance on technology, imaging, and precision workflows. Methods: This narrative review synthesizes recent literature and examples of IoMT applications relevant to radiation oncology. Areas of focus include device and sensor integration, AI-enhanced decision oncology. Areas of focus include device and sensor integration, AI-enhanced decision-support platforms, and clinical use cases such as adaptive radiotherapy and real-time toxicity monitoring. Results: When combined with artificial intelligence (AI), particularly generative AI, IoMT systems become decision-support platforms that enable adaptive radiotherapy, predictive maintenance, real-time toxicity monitoring, and patient-specific treatment planning. Emerging applications include synthetic imaging generation for MRI-only workflows, digital twins that simulate patient-specific treatment responses, and large language models for clinical education and documentation support. Conclusions: This review outlines the architecture, practical applications, and future directions of AI-powered IoMT in radiation oncology. It highlights how the convergence of IoMT and AI enables more personalized, efficient, and proactive care. However, barriers remain—including cybersecurity risks, data interoperability, usability concerns, and lack of reimbursement—that must be addressed to ensure broad adoption.