用大型语言模型来处理慢性疾病管理任务:范围审查
Henry Mukalazi Serugunda1, Ouyang Jianquan2, Hasifah Kasujja Namatovu3
1Department of Information Technology, School of Computing and Informatics Technology, Makerere University, Kampala, Uganda.
JMIR medical informatics
|September 29, 2025
概括
大型语言模型 (LLM) 对慢性疾病管理具有前景,有助于患者和从业人员. 解决准确性和道德等挑战对于安全和公平地融入医疗保健至关重要.
科学领域:
- 数字健康数字健康
- 医疗保健中的人工智能
- 慢性疾病管理 慢性疾病管理
背景情况:
- 慢性疾病给医疗保健带来了重大挑战,需要有效的管理策略.
- 数字技术,包括可穿戴设备和应用程序,是常见的,但大型语言模型 (LLM) 提供了新的潜力.
- 在慢性疾病管理中LLMs的应用和挑战需要进一步探索.
研究的目的:
- 在慢性疾病管理中对大型语言模型 (LLM) 应用进行范围审查.
- 确定当前的用途,挑战,并为LLM集成提出建议.
- 综合了最近关于LLM这一领域的初级研究的发现.
主要方法:
- 在PubMed,IEEE Xplore,Scopus和谷歌学者的系统搜索 (2023年1月至2025年1月).
- 包括29项符合特定标准的初级研究.
- 根据PRISMA-Scoping指南,对提取的数据进行主题分析.
主要成果:
- 用于患者教育,自我管理和情绪支持的LLM (62%,28%,14%).
- 在临床决策和预测方面,LLM还支持从业者 (28%,21%).
- 主要挑战包括响应准确性 (62%),数据限制 (21%),可用性 (31%),以及道德/监管问题 (35%).
结论:
- 临床医学证明了改善患者和医疗保健提供者慢性疾病管理的巨大潜力.
- 解决准确性,数据,可用性和伦理问题对于安全和公平的LLM部署至关重要.
- 未来的研究应侧重于将LLM与现有技术相结合,并开发强大的评估框架.
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