Speaker
Description
Stine S Korreman, PhD, is Professor of Medical Physics at Aarhus University, Denmark.
She leads a research group on AI for medical image analysis in radiotherapy with focus on segmentation and dose prediction, and translation of results from research to clinical practice.
She is director of the research center AIM@CANCER: AI-driven trustworthy foundation Models for personalized and effective CANCER radiotherapy
She is chair of the European Society for Radiotherapy and Oncology (ESTRO) focus group on AI in radiotherapy and director of the ESTRO course on AI in radiotherapy.
Summary
Artificial intelligence (AI) is transforming radiotherapy, with applications spanning the entire treatment pathway from image acquisition and segmentation to treatment planning, outcome prediction, and clinical decision support. While several AI technologies have matured into clinically deployed tools, many promising developments remain at the stage of methodological research or early clinical evaluation. Understanding both the opportunities and the limitations of AI is therefore essential for the next generation of researchers and clinicians.
This lecture will provide a broad overview of the current AI landscape in radiotherapy, highlighting applications across imaging, treatment planning, adaptive treatment strategies, and prediction of treatment response and toxicity. Particular emphasis will be placed on readiness for implementation and the requirements for further methodological and technical development, as well as clinical testing. The lecture will discuss key challenges including data quality and availability, robustness and generalizability, uncertainty quantification and explainability, prospective clinical testing, and integration into existing clinical workflows.
Looking beyond current applications, the lecture will identify outstanding research questions that are likely to shape the coming decade. These include multimodal models, agentic and generative models, federated and privacy-preserving learning, and the development of trustworthy AI systems. Finally, pathways for successful clinical implementation will be discussed, emphasizing multidisciplinary collaboration, rigorous evaluation, and evidence generation through prospective studies.
The lecture aims to provide participants with a structured framework for understanding the current state of AI in radiotherapy while highlighting the scientific and translational challenges that must be addressed to realize its full clinical potential.