Lema, Kennedy2024-03-132024-03-132023-10-20https://doi.org/10.31730/osf.io/xytzmhttps://africarxiv.ubuntunet.net/handle/1/420https://doi.org/10.60763/africarxiv/379https://doi.org/10.60763/africarxiv/379https://doi.org/10.60763/africarxiv/379Artificial General Intelligence (AGI) has garnered worldwide attention as a transformative technology, thanks to the emergence of groundbreaking Large AI Models (LAMs), including Large Language Models, Large Vision Models, and Large Multi-Modal Models. AGI represents an ambitious endeavor to replicate human intelligence within computer systems, making it a pivotal technology poised to revolutionize Medical Training. Fueled by recent advancements in large pre-trained models, AGI signifies a remarkable stride in empowering machines to perform tasks demanding human-level intelligence. These tasks encompass reasoning, problem solving, decision-making, and even the comprehension of human emotions and social interactions. This work conducts a comprehensive exploration of AGI, elucidating its fundamental concepts, capabilities, scope, and transformative potential in the realm of Medical Education and Training. It specifically delves into Medical Simulation Environments, Interactive Virtual Labs, Humanoid Robots in Medical Education, Continuing Medical Education (CME), Personalized Learning Pathways, Intelligent Tutoring Systems, Natural Language Processing for Medical Texts, Clinical Decision Support, and Automated Assessment Tools. The examination encompasses a thorough analysis of the prospective advantages, challenges, limitations, risks, and ethical considerations that AGI poses to Medical education and training programs, as well as its implications for Medical educators. The development of AGI necessitates fostering interdisciplinary collaboration between educators and AI engineers to propel research and application endeavors in this transformative field.Artificial general intelligenceArtificial intelligenceChatgptHealth educationHumanoid robotsLAMsMedical education and trainingNLP