| Tailored Strategies for Applying Large Language Models in Clinical Settings and Addressing Data Security Challenges |
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Tae Joon Jun |
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Department of Information Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea |
| 의료 현장 맞춤형 거대 언어 모델 활용 전략과 데이터 보안 대응 방안 탐색 |
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전태준 |
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울산대학교 의과대학 서울아산병원 정보의학과 |
Correspondence:
Tae Joon Jun, Tel: +82-2-3010-8642, Fax: +82-2-3010-8642, Email: taejoon@amc.seoul.kr
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Received: 27 May 2025 • Revised: 24 July 2025 • Accepted: 28 July 2025 |
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| Abstract |
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Since the advent of ChatGPT in 2022, large language models (LLMs) have rapidly evolved, and their clinical applications are currently being explored. This paper introduces three practical strategies for applying LLMs in healthcare settings: text-to-text, any-to-text, and retrieval-augmented generation. Each strategy is described using real-world examples and analyzed for potential data security risks. Although LLMs offer promising efficiency and performance benefits, they also pose new challenges regarding privacy and information leakage, particularly when trained using sensitive patient data. We propose tailored learning and governance approaches to mitigate such risks, emphasizing the necessity of de-identification techniques and robust guardrails for ensuring safe and effective deployment in clinical settings. |
| Key Words:
Large language models; Computer security; Generative artificial intelligence |
| 주제어:
거대 언어 모델; 컴퓨터 보안; 생성형 인공지능 |
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