AI Foundations in China's Medical Physiology Education: Pedagogical Practices and Systemic Challenges

The integration of artificial intelligence (AI) into China's medical physiology education has emerged as a transformative strategy to modernize pedagogical practices and address systemic challenges in healthcare training. Current initiatives leverage AI-driven tools such as machine learning algorithms for physiological simulations and virtual reality (VR) for immersive clinical training, aiming to standardize educational outcomes, enhance student engagement, and improve readiness for complex clinical scenarios. However, the rapid adoption of AI introduces critical challenges, including widening resource disparities between urban and rural institutions due to over-reliance on technology, risks to data privacy in educational and clinical data management, and potential erosion of humanistic care in training environments. These challenges are further compounded by concerns over academic integrity-evidenced by student use of large language models (LLMs) to substitute critical thinking in assessments-and the need to balance AI efficiency with traditional teaching methods, particularly in specialized fields like Traditional Chinese Medicine (TCM) where master-apprentice models remain foundational. To reconcile technological innovation with educational integrity, this review proposes a balanced framework encompassing five key strategies: adaptive curriculum design that synergizes AI tools with human oversight, ethical governance to ensure algorithmic transparency and data security, equitable resource distribution to bridge regional gaps, faculty development programs to enhance AI literacy, and mentorship ecosystems that integrate AI with personalized guidance. By harmonizing AI's potential with core pedagogical values, these strategies aim to cultivate a new generation of clinicians equipped with both technical proficiency and ethical discernment, ultimately advancing healthcare quality and accessibility across China.