Artificial Intelligence (AI) has been increasingly integrated into medical education, with notable achievements in postgraduate training and clinical practice. However, its application remains limited in early undergraduate education, particularly in foundational morphology disciplines such as anatomy, histology, and embryology. This study aims to address the challenges faced by medical undergraduates during their initial learning stages, including insufficient cultivation of medical thinking, passive adoption of emerging educational resources, and slow progression in foundational knowledge integration. By analyzing these issues, we propose that AI can alleviate institutional teaching burdens, resolve student learning dilemmas, and establish a robust foundation for future clinical competencies through personalized teaching pathways, virtual scenario simulations, and real-time feedback mechanisms. Furthermore, this paper provides actionable recommendations for optimizing AI implementation in foundational morphology curricula, emphasizing the balance between technological innovation and pedagogical integrity.