Prospects and applications of artificial intelligence and large language models in obstetrics and gynecology education: a narrative review

Abstract

Purpose: This review examines how artificial intelligence (AI) and large language models (LLMs) can meet the diverse demands of obstetrics and gynecology education. Based on an exploration of their applications, benefits, and challenges, strategies are proposed for effectively integrating these emerging technologies into educational programs. Current Concepts: Traditional obstetrics and gynecology education relies on lectures, hands-on training, and clinical exposure. However, these approaches often face limitations such as restricted practical opportunities and difficulties in remaining current with rapidly evolving medical knowledge. Recent AI advancements offer enhanced data analysis and problem-solving capabilities, while LLMs, through natural language processing, can supply timely, disease-specific information and facilitate simulation-based training. Despite these benefits, concerns persist regarding data bias, ethical considerations, privacy risks, and potential disparities in healthcare access. Discussion and Conclusion: Although AI and LLMs hold promise for improving obstetrics and gynecology education by expanding access to current information and reinforcing clinical competencies, they also present drawbacks. Algorithmic transparency, data quality, and ethical use of patient information must be addressed to foster trust and effectiveness. Strengthening ethics education, developing Explainable AI, and establishing clear validation and regulatory frameworks are critical for minimizing risks such as over-diagnosis, bias, and inequitable resource distribution. When used responsibly, AI and LLMs can revolutionize obstetrics and gynecology education by enhancing teaching methods, promoting student engagement, and improving clinical preparedness.