神经学中的人口健康和人工智能和大型语言模型的变革性承诺
Valdery Moura Junior1, Benjamin Kummer2,3, Lidia M V R Moura4,5
1Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.
Seminars in neurology
|March 21, 2025
概括
人口健康策略正在通过整合公共卫生,人工智能和数据分析来彻底改变神经学,以促进整个社区的大脑健康. 这种方法优先考虑预防,早期干预和为所有人提供公平的护理.
科学领域:
- 神经学 神经学
- 公共卫生 公共卫生
- 生物医学信息学 生物医学信息学
背景情况:
- 神经学正在转向人口健康战略.
- 需要系统的方法来实现全社区的神经健康.
- 传统的反应性护理模式是不够的.
研究的目的:
- 检查人口健康在神经学中的不断扩大的作用.
- 强调公共卫生原则和先进技术的整合.
- 在社区层面探索改善神经健康结果的策略.
主要方法:
- 公共卫生和神经病学的跨学科培训.
- 政策改革和生物医学信息学的实施.
- 应用人工智能 (AI) 和大型语言模型 (LLM).
- 使用预测分析,数字双胞胎和人工智能增强的诊断.
- 利用可穿戴技术,远程医疗和移动诊所.
主要成果:
- 神经学正在采用一个整体的视角,解决健康的社会决定因素.
- 先进的数据分析和跨部门的合作正在加强护理.
- 创新的应用程序正在实现主动的,数据驱动的干预.
- 改善获得护理的机会,特别是对服务不足的人群.
- 对疾病进展和社区特异性风险因素的细微见解.
结论:
- 利用技术和跨学科专业知识可以减少神经系统疾病的负担.
- 优先考虑数据驱动干预措施中的公平性至关重要.
- 可持续的,以患者为中心的护理模式有利于个人和社区.
- 培养一个未来,大脑健康是预防性的,可访问的,公平的.
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