Jung Kyu-Hwan
Department of Medical Device Management and Research, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, Korea.
Smart Healthcare Research Institute, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Korea.
Healthc Inform Res. 2025 Apr;31(2):114-124. doi: 10.4258/hir.2025.31.2.114. Epub 2025 Apr 30.
This study presents a comprehensive review of the clinical applications, technical challenges, and ethical considerations associated with using large language models (LLMs) in medicine.
A literature survey of peer-reviewed articles, technical reports, and expert commentary from relevant medical and artificial intelligence journals was conducted. Key clinical application areas, technical limitations (e.g., accuracy, validation, transparency), and ethical issues (e.g., bias, safety, accountability, privacy) were identified and analyzed.
LLMs have potential in clinical documentation assistance, decision support, patient communication, and workflow optimization. The level of supporting evidence varies; documentation support applications are relatively mature, whereas autonomous diagnostics continue to face notable limitations regarding accuracy and validation. Key technical challenges include model hallucination, lack of robust clinical validation, integration issues, and limited transparency. Ethical concerns involve algorithmic bias risking health inequities, threats to patient safety from inaccuracies, unclear accountability, data privacy, and impacts on clinician-patient interactions.
LLMs possess transformative potential for clinical medicine, particularly by augmenting clinician capabilities. However, substantial technical and ethical hurdles necessitate rigorous research, validation, clearly defined guidelines, and human oversight. Existing evidence supports an assistive rather than autonomous role, mandating careful, evidence-based integration that prioritizes patient safety and equity.
本研究全面综述了在医学中使用大语言模型（LLMs）的临床应用、技术挑战和伦理考量。
对相关医学和人工智能期刊上的同行评议文章、技术报告及专家评论进行文献调查。确定并分析了关键临床应用领域、技术局限性（如准确性、验证、透明度）以及伦理问题（如偏差、安全性、问责制、隐私）。
大语言模型在临床文档辅助、决策支持、患者沟通和工作流程优化方面具有潜力。支持证据的水平各不相同；文档支持应用相对成熟，而自主诊断在准确性和验证方面仍面临显著限制。关键技术挑战包括模型幻觉、缺乏强大的临床验证、集成问题以及透明度有限。伦理问题涉及算法偏差导致健康不平等的风险、不准确对患者安全的威胁、问责不明确、数据隐私以及对医患互动的影响。
大语言模型对临床医学具有变革潜力，尤其是通过增强临床医生的能力。然而，重大的技术和伦理障碍需要进行严格的研究、验证、明确的指导方针以及人为监督。现有证据支持其辅助而非自主的角色，要求进行谨慎的、基于证据的整合，将患者安全和公平放在首位。
