The integration of Artificial Intelligence (AI) into healthcare is transforming medical education, reshaping how diagnostic skills, treatment approaches, and patient care methods are taught. This study investigates the interface of AI and medical education, focusing on the preparedness and views of clinical educators. Using the Unified Theory of Acceptance and Use of Technology as a framework, this research assesses the factors influencing AI adoption in medical training, including performance expectancy, effort expectancy, social influence, and facilitating conditions. Through an inductive-to-deductive methodology, we conducted semi-structured interviews with 15 clinical educators from the south-central region of the United States who oversee third-year medical students. Key findings of teacher readiness at the interface of AI and medical education centered around 1) the technological learning curve, 2) the need for hands-on, action-based learning, 3) the critical role of institutional support, 4) mentorship as a crucial support system, 5) balancing human elements with AI integration, and 6) divergent comfort levels between generational cohorts. While AI holds promise to reform medical education by fostering adaptive, personalized learning environments, it also raises challenges in preserving essential human elements of patient care. Addressing these challenges demands a strategic, institutionally supported shift in medical pedagogy to ensure that AI integration is both effective and sustainable. The study’s insight into clinical educators' perspectives lay the groundwork for developing AI-ready educational models that balance technical expertise with core humanistic values, supporting a comprehensive approach to medical training in the AI-driven future.