在临床实践中实施LLM的挑战:观点
Yaara Artsi1, Vera Sorin2, Benjamin S Glicksberg3,4,5
1Azrieli Faculty of Medicine, Bar-Ilan University, Zefat 1311502, Israel.
Journal of clinical medicine
|September 13, 2025
概括
大型语言模型 (LLM) 在医疗保健中提供了变革性的潜力,但面临着重大实施障碍. 处理工作流程,偏见,监管,技术风险和患者信任对于负责任的临床整合至关重要.
科学领域:
- 人工智能在医学中的应用
- 临床信息学 临床信息学
- 医疗保健技术 技术 医疗保健 技术
背景情况:
- 大型语言模型 (LLM) 显示出对医疗保健任务如文档,诊断和患者沟通等革命性的承诺.
- 尽管有潜在的好处,但在临床环境中广泛采用LLM仍面临重大障碍.
研究的目的:
- 探索和综合阻碍LLMs融入临床实践的关键障碍.
- 提出一个框架,以了解和解决在医疗保健中部署LLM的技术,伦理和实际挑战.
主要方法:
- 一个全面的审查和综合最近的证据.
- 分析五个关键领域的实施挑战:工作流程/安全,偏见/公平,治理,技术漏洞和患者信任.
- 基于经验研究的概念框架的开发.
主要成果:
- 确定了五个主要的挑战领域:工作流失调和诊断安全,偏见和公平问题,监管和法律治理差距,技术漏洞 (例如幻觉,数据中毒),以及保持患者的信任和人类联系.
- 突出了LLM能力和缓解策略的快速发展,表明短期临床影响的潜力.
结论:
- 在医疗保健中成功整合LLM需要采取多方面的方法来解决已识别的障碍.
- 未来的研究,强有力的治理以及对负责任的部署的关注对于在临床环境中实现LLM的好处至关重要.
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