Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
Br J Radiol. 2019 Nov;92(1103):20190389. doi: 10.1259/bjr.20190389. Epub 2019 Jul 26.
In the era of personalized medicine, the emphasis of health care is shifting from populations to individuals. Artificial intelligence (AI) is capable of learning without explicit instruction and has emerging applications in medicine, particularly radiology. Whereas much attention has focused on teaching radiology trainees about AI, here our goal is to instead focus on how AI might be developed to better teach radiology trainees. While the idea of using AI to improve education is not new, the application of AI to medical and radiological education remains very limited. Based on the current educational foundation, we highlight an AI-integrated framework to augment radiology education and provide use case examples informed by our own institution's practice. The coming age of "AI-augmented radiology" may enable not only "precision medicine" but also what we describe as "precision medical education," where instruction is tailored to individual trainees based on their learning styles and needs.
在个性化医疗时代,医疗保健的重点正从人群转向个体。人工智能(AI)能够在没有明确指导的情况下进行学习,并且在医学,特别是放射学领域有新兴的应用。虽然人们已经关注到了如何教导放射科学员使用 AI,但在这里,我们的目标是转而关注如何开发 AI 以更好地教导放射科学员。虽然使用 AI 来改善教育的想法并不新鲜,但 AI 在医学和放射学教育中的应用仍然非常有限。基于当前的教育基础,我们强调了一个 AI 集成框架,以增强放射科教育,并提供了我们自己机构实践的用例示例。即将到来的“AI 增强放射学”时代不仅可以实现“精准医学”,还可以实现我们所描述的“精准医学教育”,即根据学员的学习风格和需求,为其量身定制教学内容。