设计对话智能:大型语言模型 (GPT驱动) 平台对精确的母亲和新生儿健康参与的影响:系统审查
Robab Rasoli1, Fahimeh Ebrahimisadrabadi2, Zahra Khedri3
1School of Nursing and Midwifery, Department of Midwifery, Zanjan University of Medical Sciences, Zanjan, Iran.
Oxford open digital health
|February 4, 2026
概括
大型语言模型 (LLM) 和生成预训练变压器 (GPT) 为孕产妇和新生儿护理提供了可扩展的数字健康工具. 这些由人工智能驱动的聊天机器人提高了参与度,提供了支持,并帮助检测风险,以获得更好的健康结果.
科学领域:
- 数字健康和人工智能在孕产妇和新生儿护理中的作用
背景情况:
- 由于获得及时,个性化和支持性护理的机会有限,母亲和新生儿死亡率在资源较低的环境中仍然很高.
- 大型语言模型 (LLM) 和生成预训练转换器 (GPT) 提供可扩展的数字健康解决方案,用于基于证据的信息,心理健康支持和风险分层.
研究的目的:
- 系统地审查关于LLM/GPT驱动聊天机器人的设计,实施和有效性的全球证据,以实现精确的母亲和新生儿健康参与.
- 综合这些人工智能工具在产前护理中的能力和影响的发现.
主要方法:
- 按照PRISMA 2020指南进行系统审查,从2015年1月到2025年11月搜索多个数据库 (MEDLINE,Embase,CINAHL,Web of Science,Inspec,IEEE Xplore).
- 包括15项同行评审研究 (发表于2021-2025年) 对孕妇和伴侣评估基于GPT的对话药物.
- 使用EQUATOR工具和AI特定框架进行质量评估,由两名审查员进行独立的偏差风险评估.
主要成果:
- 与一般的LLM/GPT系统相比,LLM/GPT驱动的聊天机器人表现出更高的对话自然性,主题多样性和用户满意度 (85-94%的可接受性).
- 关键功能包括精神教育 (14项研究),心理健康查 (11/15),伴侣参与 (6/15),以及对不良结果的风险建模 (9项研究).
- 对于妊娠糖尿病 (AUC 0.88-0.94) 和孕前症状,与临床医生的诊断一致性很高;在6个月内,用户保留率为62-78%,在服务不足的人群中参与度很强. 没有报告严重伤害.
结论:
- 由LLM/GPT驱动的对话代理是可访问,个性化的孕产妇和新生儿健康支持的突破,有效地解决高风险怀孕护理,情感支持和风险检测方面的差距.
- 这些人工智能工具展示了安全性,文化适应性和潜在的重大影响,特别是在低收入和中等收入环境中.
- 快速整合LLM/GPT驱动的聊天机器人进入常规产前护理途径得到了证据的强烈支持.
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