为生物医学领域打造个性化AI训练体系，融合真实项目与导师网络。
Bridge2AI: Building A Cross-disciplinary Curriculum Towards AI-Enhanced Biomedical and Clinical Care
- 基于学习健康系统框架，设计跨学科课程
- 覆盖30名学者、100名导师，支持个性化培养路径
- 通过反馈迭代优化，适合医工交叉人才成长
随着人工智能在医疗领域的日益重要，迫切需要个性化且可适应的生物信息学与生物医学培训体系。美国国立卫生研究院（NIH）Bridge2AI培训、招募与指导（TRM）工作组开发了一套以协同创新、伦理数据管理及职业发展为核心的跨学科课程，融入学习健康系统（LHS）框架。课程包含基础AI模块、真实世界项目以及贯穿桥接2AI重大挑战和桥中心的结构化师生网络。基于六个学习者角色画像，项目定制教育路径，兼顾可扩展性。通过持续反馈驱动的迭代优化，确保内容紧跟学习进展与新兴趋势。目前已有超过30名学者和100名导师参与，覆盖北美地区。结果表明，该模型能够有效构建跨学科能力，推动整合性、伦理导向的生物医学人工智能教育。
原文摘要 · Abstract (English)
Objective: As AI becomes increasingly central to healthcare, there is a pressing need for bioinformatics and biomedical training systems that are personalized and adaptable. Materials and Methods: The NIH Bridge2AI Training, Recruitment, and Mentoring (TRM) Working Group developed a cross-disciplinary curriculum grounded in collaborative innovation, ethical data stewardship, and professional development within an adapted Learning Health System (LHS) framework. Results: The curriculum integrates foundational AI modules, real-world projects, and a structured mentee-mentor network spanning Bridge2AI Grand Challenges and the Bridge Center. Guided by six learner personas, the program tailors educational pathways to individual needs while supporting scalability. Discussion: Iterative refinement driven by continuous feedback ensures that content remains responsive to learner progress and emerging trends. Conclusion: With over 30 scholars and 100 mentors engaged across North America, the TRM model demonstrates how adaptive, persona-informed training can build interdisciplinary competencies and foster an integrative, ethically grounded AI education in biomedical contexts.
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