Artificial Intelligence in Medical Education: Current Research and Applications

As medical education undergoes continuous transformation, the integration of artificial intelligence (AI) into teaching practices has attracted growing attention. AI has demonstrated considerable potential in virtual simulation, personalized learning support, clinical reasoning training, humanistic education, and interdisciplinary teaching. These applications have been shown to enhance instructional quality, optimize the learning experience, and foster educational innovation. Evidence indicates that AI not only delivers real-time feedback and targeted training but also facilitates the development of students' cognitive abilities and practical skills in complex clinical scenarios. Nevertheless, its widespread adoption faces persistent challenges, including limited acceptance among faculty and students, unequal distribution of resources, inadequate assessment frameworks, and lagging institutional support. Future progress should emphasize large-scale, multicenter empirical studies, promote deeper integration of technology with curricula, strengthen resource sharing and interdisciplinary talent cultivation, and establish unified standards and quality assurance systems. Such measures are essential for achieving the sustainable implementation of AI in medical education and maximizing its educational value.