大型语言模型在初级医疗保健中的机遇和挑战
Hongyang Qin1,2, Yuling Tong1
1The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.
Journal of primary care & community health
|March 31, 2025
概括
大型语言模型 (LLM) 为应对中国初级卫生保健 (PHC) 系统的挑战提供了巨大的潜力,改善了从疾病预防到慢性护理的服务. 伦理和法律框架对于实现全民健康覆盖的安全和公平的人工智能集成至关重要.
科学领域:
- 公共卫生 公共卫生
- 人工智能在医学中的应用
- 卫生系统研究 卫生系统研究
背景情况:
- 初级卫生保健 (PHC) 对全民健康覆盖至关重要,但在中国面临挑战,包括资源不平等,劳动力短缺和慢性疾病管理.
- 人工智能 (AI),特别是大型语言模型 (LLM),由于先进的自然语言处理和推理,有望提高医疗服务.
研究的目的:
- 探索LLMs在中国初级卫生保健 (PHC) 系统中的潜在应用.
- 确定务实的障碍,并提出未来的方向,LLM整合在PHC.
主要方法:
- 审查在PHC中潜在的LLM应用,包括健康促进,疾病预防,咨询,诊断,分拣,慢性疾病管理和心理健康支持.
- 分析实用障碍,如透明度,数据隐私,社会偏见和结果误导.
- 讨论未来的发展需求,强调跨学科合作,健康公平以及道德/法律框架.
主要成果:
- 在中国,LLM可能会增强医疗保健服务的各个方面.
- 为了有效实施LLM,需要解决包括透明度,隐私和偏见在内的重大务实障碍.
- 发展道德和法律框架对于在PHC中安全和公平地部署AI至关重要.
结论:
- 士学位为改善中国的初级医疗保健系统和推进全民医疗覆盖提供了一个有前途的途径.
- 解决道德,法律和实际挑战对于成功的LLM整合至关重要.
- 未来的努力应侧重于跨学科合作,资源共享和对医疗保健中的AI的强有力的治理.
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