Bridge2AI: Building A Cross-disciplinary Curriculum Towards AI-Enhanced Biomedical and Clinical Care
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , , , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866916747674648576 |
|---|---|
| author | Rincon, John Pelletier, Alexander R. Gilliland, Destiny Wang, Wei Wang, Ding Sankar, Baradwaj S. Scott-Sheldon, Lori Gebreab, Samson Hersh, William Rashidi, Parisa Baxter, Sally Schulz, Wade Ideker, Trey Bensoussan, Yael Boutros, Paul C. Bui, Alex A. T. Walsh, Colin Watson, Karol E. Ping, Peipei |
| author_facet | Rincon, John Pelletier, Alexander R. Gilliland, Destiny Wang, Wei Wang, Ding Sankar, Baradwaj S. Scott-Sheldon, Lori Gebreab, Samson Hersh, William Rashidi, Parisa Baxter, Sally Schulz, Wade Ideker, Trey Bensoussan, Yael Boutros, Paul C. Bui, Alex A. T. Walsh, Colin Watson, Karol E. Ping, Peipei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_14757 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bridge2AI: Building A Cross-disciplinary Curriculum Towards AI-Enhanced Biomedical and Clinical Care Rincon, John Pelletier, Alexander R. Gilliland, Destiny Wang, Wei Wang, Ding Sankar, Baradwaj S. Scott-Sheldon, Lori Gebreab, Samson Hersh, William Rashidi, Parisa Baxter, Sally Schulz, Wade Ideker, Trey Bensoussan, Yael Boutros, Paul C. Bui, Alex A. T. Walsh, Colin Watson, Karol E. Ping, Peipei Computers and Society Artificial Intelligence 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. |
| title | Bridge2AI: Building A Cross-disciplinary Curriculum Towards AI-Enhanced Biomedical and Clinical Care |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2505.14757 |