Interdisciplinary learning enhances competencies in artificial intelligence and health education, suggesting a paradigm shift.
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.
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Zhang et al. (2025) studied this question.
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