Abstract
Visual learning, through graphics, diagrams, and other visual tools, has been shown to significantly enhance information retention, with studies indicating that up to 83% of learning is visual. In the field of radiation oncology, where continuous education is critical to the safe and effective treatment of cancer, the complexity and text-heavy nature of traditional resources can pose barriers to effective learning. This editorial examines the transformative potential of generative artificial intelligence (AI) in supporting cancer care professionals by enhancing comprehension of radiation oncology documents through tailored, visual learning modules. Using the AAPM TG-100 report "Application of risk analysis methods to radiation therapy quality management" as a proof of concept, the authors first developed web-based infographics manually and then demonstrated how AI tools such as ChatGPT, ClickUp, and NotebookLM dramatically expedite the process. These tools not only automate the creation of high-quality visuals but also support personalized and multimodal learning, including AI-generated podcasts for auditory learners. By making complex oncology-specific content more accessible, AI empowers radiation oncology clinicians and trainees to better understand, implement, and innovate in cancer treatment.