Problem-Based Learning (PBL) has significantly impacted biomedical engineering (BME) education since its introduction in the early 2000s, effectively enhancing critical thinking and real-world knowledge application among students. With biomedical engineering rapidly converging with artificial intelligence (AI), integrating effective AI education into established curricula has become challenging yet increasingly necessary. Recent advancements, including AI's recognition by the 2024 Nobel Prize, have highlighted the importance of training students comprehensively in biomedical AI. However, effective biomedical AI education faces substantial obstacles, such as diverse student backgrounds, limited personalized mentoring, constrained computational resources, and difficulties in safely scaling hands-on practical experiments due to privacy and ethical concerns associated with biomedical data. To overcome these issues, we conducted a three-year (2021-2023) case study implementing an advanced PBL framework tailored specifically for biomedical AI education, involving 92 undergraduate and 156 graduate students from the joint Biomedical Engineering program of Georgia Institute of Technology and Emory University. Our approach emphasizes collaborative, interdisciplinary problem-solving through authentic biomedical AI challenges. The implementation led to measurable improvements in learning outcomes, evidenced by high research productivity (16 student-authored publications), consistently positive peer evaluations, and successful development of innovative computational methods addressing real biomedical challenges. Additionally, we examined the role of generative AI both as a teaching subject and an educational support tool within the PBL framework. Our study presents a practical and scalable roadmap for biomedical engineering departments aiming to integrate robust AI education into their curricula.

翻译：自21世纪初引入以来，问题导向学习（PBL）已对生物医学工程（BME）教育产生了显著影响，有效提升了学生的批判性思维和现实世界知识应用能力。随着生物医学工程与人工智能（AI）的快速融合，将有效的AI教育整合到既定课程体系中变得具有挑战性，但也日益必要。最近的进展，包括AI获得2024年诺贝尔奖的认可，突显了在生物医学AI领域全面培养学生的重要性。然而，有效的生物医学AI教育面临着重大障碍，例如学生背景多样、个性化指导有限、计算资源受限，以及由于生物医学数据相关的隐私和伦理问题，难以安全地扩展实践性动手实验。为克服这些问题，我们进行了一项为期三年（2021-2023年）的案例研究，实施了一个专为生物医学AI教育量身定制的高级PBL框架，涉及佐治亚理工学院与埃默里大学联合生物医学工程项目的92名本科生和156名研究生。我们的方法强调通过真实的生物医学AI挑战进行协作式、跨学科的问题解决。该实施带来了学习成果的可衡量提升，具体表现为高研究产出（16篇学生署名出版物）、持续积极的同行评价，以及成功开发了应对真实生物医学挑战的创新计算方法。此外，我们探讨了生成式AI在PBL框架中既作为教学科目又作为教育支持工具的角色。本研究为旨在将稳健的AI教育整合到其课程中的生物医学工程系提供了一个实用且可扩展的路线图。