Automated radiotherapy treatment planning and data-driven oncology – Bringing AI into clinical radiotherapy

Abstract:

Recent advances in artificial intelligence have established machine learning as a clinically viable technology across multiple domains of radiation oncology. Consequently, attention is increasingly focused on the implementation, validation and long-term integration of AI within routine clinical workflows. This presentation explores current applications of machine learning in radiotherapy, including automated contouring, patient-specific dose prediction, treatment planning automation and population-based clinical analytics. The role of these technologies in enabling data-driven decision support and continuous learning healthcare systems will also be discussed.

About Christian:

Dr. Christian Hahn is a medical physicist and machine learning application specialist at RaySearch Laboratories AB. His focus lies on the clinical applications of machine learning, treatment planning automation and the development of data-driven workflows in radiation oncology. As part of the machine learning service team, he works with clinical and academic institutions worldwide to develop, implement and monitor AI-based solutions in routine cancer care. His current focus is on translating advances in deep learning treatment planning and oncology analytics into clinical workflows that improve treatment efficiency, consistency, quality and patient outcome.