Bridge2AI: Building A Cross-disciplinary Curriculum Towards AI-Enhanced Biomedical and Clinical Care
Project Overview
The document highlights the Bridge2AI initiative, which seeks to revolutionize biomedical and clinical education by implementing a cross-disciplinary curriculum centered around generative AI. This initiative addresses the growing demand for personalized and adaptable training systems that not only teach AI fundamentals but also incorporate ethical considerations and practical applications within the healthcare sector. The curriculum features foundational AI modules, hands-on projects, and a mentoring network designed to support a diverse range of learners and encourage collaboration among different disciplines. Key applications of generative AI in this context include the development of tailored educational experiences that respond to individual learner needs, the enhancement of real-world problem-solving skills, and the fostering of interdisciplinary teamwork. The findings indicate that such an integrated approach not only improves learners’ understanding of AI technologies but also prepares them to apply these skills ethically and effectively in real-world healthcare settings. Overall, the initiative represents a significant step towards modernizing education in the biomedical field, ensuring that future professionals are equipped with the necessary tools to navigate the complexities of AI in healthcare.
Key Applications
Bridge2AI Training, Recruitment, and Mentoring (TRM) program
Context: Educational program for scholars and professionals in biomedical AI across North America
Implementation: Developed a modular curriculum integrating foundational AI concepts, real-world projects, and a structured mentoring network.
Outcomes: Improved knowledge, practical skills, and ethical awareness among over 150 scholars. Increased confidence in applying AI in biomedical research.
Challenges: Need for continuous adaptation of curriculum to keep pace with rapid advancements in AI and healthcare.
Implementation Barriers
Educational Accessibility
Limited educational accessibility and integration with clinical practice in existing training systems.
Proposed Solutions: Developing a flexible and responsive curriculum that prioritizes cross-disciplinary collaboration and ethical data usage.
Engagement with Stakeholders
Insufficient engagement with real-world stakeholders and challenges in maintaining ongoing communication.
Proposed Solutions: Establishing regular feedback loops and structured mentorship to enhance stakeholder involvement and collaboration.
Project Team
John Rincon
Researcher
Alexander R. Pelletier
Researcher
Destiny Gilliland
Researcher
Wei Wang
Researcher
Ding Wang
Researcher
Baradwaj S. Sankar
Researcher
Lori Scott-Sheldon
Researcher
Samson Gebreab
Researcher
William Hersh
Researcher
Parisa Rashidi
Researcher
Sally Baxter
Researcher
Wade Schulz
Researcher
Trey Ideker
Researcher
Yael Bensoussan
Researcher
Paul C. Boutros
Researcher
Alex A. T. Bui
Researcher
Colin Walsh
Researcher
Karol E. Watson
Researcher
Peipei Ping
Researcher
Contact Information
For information about the paper, please contact the authors.
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
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai