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

2025-05-20

AI Summary

The deep integration of AI in healthcare urgently requires interdisciplinary professionals with biomedical literacy, AI proficiency, and ethical acumen; however, existing training frameworks lack personalization and adaptability. Method: This study establishes the first interdisciplinary educational framework for AI-augmented biomedical and clinical care, grounded in the Learning Health System (LHS) paradigm. It introduces a novel learner-persona-driven adaptive curriculum engineering approach—categorizing learners into six archetypes—to enable scalable personalization. Additionally, it pioneers a closed-loop mentorship model—the “Grand Challenges–Bridge Center”—integrating ethical data governance, collaborative innovation, and career development. Contribution/Results: Deployed across North America with 30+ scholars and 100+ mentors, the framework demonstrates empirically significant improvements in learners’ ethical decision-making capacity and interdisciplinary AI-biomedicine competency. Its design ensures scalability and broad translational potential for global AI-in-health education.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

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.

Problem

• Developing personalized AI-enhanced biomedical training systems
• Creating cross-disciplinary curriculum with ethical data stewardship
• Adapting AI education to individual needs and scalability

Innovation

• Cross-disciplinary curriculum with AI modules
• Adaptive Learning Health System framework
• Persona-tailored educational pathways