Bridge2AI: Building A Cross-disciplinary Curriculum Towards AI-Enhanced Biomedical and Clinical Care
- URL: http://arxiv.org/abs/2505.14757v1
- Date: Tue, 20 May 2025 16:19:05 GMT
- Title: Bridge2AI: Building A Cross-disciplinary Curriculum Towards AI-Enhanced Biomedical and Clinical Care
- Authors: John Rincon, Alexander R. Pelletier, Destiny Gilliland, Wei Wang, Ding Wang, Baradwaj S. Sankar, Lori Scott-Sheldon, Samson Gebreab, William Hersh, Parisa Rashidi, Sally Baxter, Wade Schulz, Trey Ideker, Yael Bensoussan, Paul C. Boutros, Alex A. T. Bui, Colin Walsh, Karol E. Watson, Peipei Ping,
- Abstract summary: The NIH Bridge2AI Working Group developed a cross-disciplinary curriculum grounded in collaborative innovation, ethical data stewardship, and professional development.<n>With over 30 scholars and 100 mentors engaged across North America, the TRM model demonstrates how adaptive, persona-informed training can build interdisciplinary competencies.
- Score: 34.695712996247835
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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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