Objective: This abstract aims to examine the integration of Artificial Intelligence (AI) into medical education, focusing on enhancing the learning experiences and teaching methodologies for medical professionals. It seeks to illustrate how AI can be a transformative tool in medical training, offering opportunities for more personalized and efficient learning.

Background: The integration of AI, including Machine Learning, Natural Language Processing, Computer Vision, and Large Language Models, represents a significant shift in medical education. Unlike traditional search engines, these AI components offer a dynamic and interactive learning environment. Instructional

Methods: We use AI to generate practice board questions, simulating real-world scenarios that students might encounter. It also assists in providing the latest medical recommendations, ensuring that learners are exposed to current best practices. AI aids in creating visually engaging materials and comparing various pathologies, enhancing the understanding of complex medical conditions. Additionally, AI plays a crucial role in designing course outlines and offering real-time feedback in virtual patient simulations, which is essential for practical learning.

Educational Outcomes: To date, the integration of AI in medical education has shown promising results. It has enhanced the efficiency and accuracy of medical training. Students are better able to understand and retain complex information, and educators can tailor their teaching methods to individual learning styles. This personalization ensures that each student receives the attention and resources they need to succeed.

Strengths and Improvements: While AI in medical education offers precision, adaptability, and a dynamic learning environment, it also presents challenges such as the potential for overreliance on technology and issues surrounding data privacy. There is a need for emphasis on training faculty to effectively integrate AI tools into teaching.