Chan Maria F, Wang Dongxu
Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Technol Cancer Res Treat. 2025 Jan-Dec;24:15330338251378314. doi: 10.1177/15330338251378314. Epub 2025 Sep 16.
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.
通过图形、图表和其他视觉工具进行的视觉学习已被证明能显著提高信息保留率,研究表明高达83%的学习是视觉性的。在放射肿瘤学领域,持续教育对于癌症的安全有效治疗至关重要,传统资源的复杂性和大量文本可能会对有效学习造成障碍。这篇社论探讨了生成式人工智能(AI)通过定制的视觉学习模块增强对放射肿瘤学文档的理解,从而支持癌症护理专业人员的变革潜力。作者以美国医学物理师协会(AAPM)TG - 100报告《风险分析方法在放射治疗质量管理中的应用》作为概念验证,首先手动开发了基于网络的信息图表,然后展示了ChatGPT、ClickUp和NotebookLM等人工智能工具如何极大地加快这一过程。这些工具不仅能自动创建高质量的视觉内容,还支持个性化和多模态学习,包括为听觉学习者生成的人工智能播客。通过使复杂的肿瘤学特定内容更易于理解,人工智能使放射肿瘤学临床医生和学员能够更好地理解、实施和创新癌症治疗。