This course provides a comprehensive exploration of the rapidly evolving field of Artificial Intelligence (AI) and its transformative impact on Radiation Oncology. The curriculum is structured to guide students from the foundational principles to hands-on application, covering three critical areas.
First, we will examine the **ethical governance and regulatory landscape** surrounding AI in medicine, ensuring a responsible understanding of its implementation. Next, we will delve into the **core clinical applications** of AI, including automated segmentation, treatment planning, and image guidance, which are revolutionizing modern radiotherapy workflows.
The course then transitions to the **AI development lifecycle**, focusing on standardization, quality assurance, and practical data handling. A significant "Magician's Corner" workshop series will equip you with the foundational skills to build, optimize, and evaluate deep learning models yourself. Finally, we will explore essential tools and platforms for AI research, from public data repositories and coding assistants to high-performance computing and large language models, preparing you to contribute to the future of AI-driven cancer care.
- Teacher: Joey Aggabao
- Teacher: Conrad Bayley
- Teacher: Bryson Dietz
- Teacher: Ali Golestani
- Teacher: Ethan Laukkanen
- Teacher: Laurence Lee
- Teacher: Derek Liu
- Teacher: Nathan Murtha
- Teacher: Ankur Sharma
- Teacher: Kota Talla
- Teacher: Tania Wood
