====== Generative language models ======

[[Artificial intelligence]] (AI) and generative [[language]] models (GLMs) present significant opportunities for enhancing medical [[education]], including the provision of realistic [[simulation]]s, digital patients, personalized feedback, [[evaluation]] methods, and the elimination of language barriers. These advanced technologies can facilitate immersive learning [[environment]]s and enhance [[medical student]]s' educational outcomes. However, ensuring content [[quality]], addressing [[bias]]es, and managing ethical and legal concerns present obstacles. To mitigate these challenges, it is necessary to evaluate the accuracy and relevance of AI-generated content, address potential biases, and develop [[guideline]]s and policies governing the use of AI-generated content in medical education. Collaboration among educators, researchers, and practitioners is essential for developing best practices, guidelines, and transparent AI models that encourage the ethical and responsible use of GLMs and AI in medical education. By sharing information about the data used for training, obstacles encountered, and evaluation methods, developers can increase their credibility and trustworthiness within the medical community. In order to realize the full potential of AI and GLMs in medical education while mitigating potential risks and obstacles, ongoing research and interdisciplinary collaboration are necessary. By collaborating, medical professionals can ensure that these technologies are effectively and responsibly integrated, contributing to enhanced learning experiences and patient care
((Karabacak M, Ozkara BB, Margetis K, Wintermark M, Bisdas S. The Advent of Generative Language Models in Medical Education. JMIR Med Educ. 2023 Jun 6;9:e48163. doi: 10.2196/48163. PMID: 37279048.))