‘Brain + X’: Interdisciplinary health professions education for the AI era
Artificial intelligence (AI) is reshaping healthcare, necessitating a transformation in health professions education. To prepare future professionals for an AI-integrated landscape, curricula must evolve beyond traditional biomedical training to incorporate interdisciplinary knowledge and AI-related competencies. However, current education often falls short in equipping students with the necessary skills. The 'Brain + X' course exemplifies the effectiveness of interdisciplinary learning in enhancing both theoretical understanding and practical AI applications. By integrating neuroscience fundamentals with AI techniques and hands-on training, the course fosters critical thinking and cross-disciplinary problem-solving skills. Participants reported significant improvements in data analysis, scientific conceptualization, and theoretical knowledge expansion. Survey data indicate that 96% of students found the course directly applicable to their research, while 91.9% demonstrated an enhanced capacity to address cross-disciplinary challenges. Pre- and post-course evaluations further revealed increased mastery of neuroscience methodologies and recognition of AI's indispensable role in healthcare. Additionally, the course strengthened students' ability to synthesize knowledge across disciplines, promoting long-term intellectual and professional growth. These findings underscore the necessity of interdisciplinary AI education in health professions. The 'Brain + X' model provides a foundation for integrating AI into healthcare training, fostering a new generation of professionals equipped to navigate and contribute to an increasingly AI-driven medical ecosystem.
Authors
- Boxing Li (ORCID: https://orcid.org/0000-0002-4497-817X)
- Xiao Min Zhang (ORCID: https://orcid.org/0000-0001-6596-2233)
- Lianyan Huang
Institutions
- Sun Yat-sen University (CN)
- The First Affiliated Hospital, Sun Yat-sen University (CN)
Publication Details
- Journal
- Medical Teacher
- Published
- 2025-09-22
- DOI
- https://doi.org/10.1080/0142159x.2025.2560570
- Citations
- 2
- Primary Topic
- Biomedical and Engineering Education
- Type
- article
- Field-Weighted Citation Impact
- 1.35