Generative artificial intelligence (Generative AI) is reshaping both learning and teaching paradigms in medical education. With the advancement of Large Language Models (LLMs)-based tools such as ChatGPT, Gemini, and other medical-domain-specific models, Generative AI shows strong potential to address persistent challenges in medical education, including rigid curricula, unequal access to educational resources, and the diverse learning needs of medical students. This review summarizes the applications of Generative AI across key domains: (1) personalized learning through real-time analysis of student performance; (2) clinical skills training via immersive simulations and virtual patients; (3) automated generation of teaching materials such as clinical cases and assessments; and  (4) support for student research and academic writing. Empirical evidence indicates that Generative AI-enhanced instruction can improve knowledge acquisition, clinical reasoning, and overall educational efficiency. However, challenges remain, including the generation of inaccurate or fabricated content, risks to academic integrity, algorithmic bias, data privacy concerns, and unresolved ethical issues regarding AI's role in teaching. Without proper oversight, these risks may compromise educational quality and equity. To ensure responsible adoption, this review advocates for the establishment of institutional policies, enhancement of educators' AI literacy, transparent model validation, and a human-centered design framework that positions Generative AI as a collaborative teaching assistant. When responsibly integrated, Generative AI holds the transformative potential to cultivate future medical professionals equipped with clinical competence, responsibility, and innovative thinking.