Bridge2AI: Building A Cross-disciplinary Curriculum Towards AI-Enhanced Biomedical and Clinical Care

TL;DR

This paper presents a cross-disciplinary AI curriculum designed for biomedical and clinical training, emphasizing personalized, ethical, and scalable education through iterative feedback and mentorship, involving over 30 scholars and 100 mentors.

Contribution

It introduces a novel adaptive, persona-based curriculum framework integrating AI, real-world projects, and mentorship within a biomedical context.

Findings

Successful engagement of over 30 scholars and 100 mentors.

Curriculum adapts to individual learner needs and emerging trends.

Demonstrates scalable, ethically grounded AI education in healthcare.

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.