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산부인과 교육에서의 인공지능 활용 가능성: 대형 언어 모델의 잠재력과 전망

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 emer...

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Published in:Journal of the Korean Medical Association / Taehan Uisa Hyophoe Chi Vol. 68; no. 3; pp. 161 - 169
Main Author: Eoh, Kyung Jin
Format: research tables/charts Journal Article
Published: Korean Medical Association Mar2025
Online Access:View this record in EBSCOhost
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      dt: Mar2025
      vid: 68
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      pub: Korean Medical Association
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        10.5124/jkma.2025.68.3.161
        184468187
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        atl: 산부인과 교육에서의 인공지능 활용 가능성: 대형 언어 모델의 잠재력과 전망
      aug:
        au: Eoh, Kyung Jin
        affil: Department of Obstetrics and Gynecology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Korea
      sug:
        subj:
          Artificial Intelligence
          Natural Language Processing
          Obstetrics Education
          Gynecology Education
          Educational Technology
          Computer-Assisted Instruction
          Problem Solving
          Access to Information
          Health Information
          Computer Simulation
          Data Analysis
          Privacy and Confidentiality
          Ethics, Medical
          Data Breach
          Health Services Accessibility
          Healthcare Disparities
      ab: 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.
      pubtype: Academic Journal
      doctype:
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: Korean
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