Session

Overview: Artificial intelligence is transforming radiation oncology, driving advances in fields such as medical imaging, tumor segmentation, and decision support. This session brings together experts at the forefront of AI applications, showcasing the latest innovations in these fields. Kareem Abdul Wahid will open the session with insights into how data science competitions accelerate AI advancements in radiation oncology, with a focus on the HNTS-MRG 2024 Challenge for MRI-guided tumor segmentation. He will discuss how benchmarking competitions accelerate the development of deep learning models, highlighting key takeaways on architecture choices, ensembling strategies, and training methodologies. Matteo Maspero will then explore the role of physics-informed neural networks (PINNs) in synthetic CT (sCT) generation from MRI. He will explain how incorporating physical priors enhances AI model performance, discuss applications in quantitative MRI and radiotherapy planning, and examine future potential for expanding physics-informed models in medical imaging. Expanding the discussion to broader applications, Ana Maria Barragan Montero will provide a comprehensive overview of foundation models in radiotherapy, discussing their advantages over traditional architectures like UNet. She will explore applications in medical imaging, multimodal models, and address critical challenges in model trustworthiness, reliability, and sustainability. Finally, Florian Putz will showcase the emerging role of large language models (LLMs) in radiation oncology, from decision support and automated documentation to multimodal AI-powered research tools. He will highlight LLM integration with oncology information systems, workflow automation, and privacy-preserving AI solutions for clinical applications. This symposium provides a cohesive journey from data-driven AI innovations to foundational AI principles and real-world clinical applications. Attendees will gain a comprehensive understanding of how the latest AI advancements are transforming radiation oncology and shaping its future.