人工智能工具和证据综合平台的景观:范围审查
M Sharmila A Sousa1, Sasha Peiris2, Mabel F Figueiró3
1Department of Medicine, Department of Morphology and Genetics, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil. sharmila.sousa@gmail.com.
Systematic reviews
|February 10, 2026
概括
人工智能 (AI) 工具,包括大语言模型 (LLM),可以加速证据合成 (ES),但需要仔细评估. 本范围审查对ES的AI工具进行了映射,在137项研究中确定了388种工具,以促进公平的访问和负责任的实施.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 证据综合 (ES) 对于在医疗技术评估,指导方针和政策中基于证据的决策至关重要.
- 传统的ES方法是耗时的,需要几个月或几年和大量的手工努力.
- 技术进步,特别是人工智能和LLM,为自动化ES步骤,提高效率和降低成本提供了潜力.
研究的目的:
- 为了进行第一个范围审查,绘制数据科学工具,包括LLM,用于优化ES步骤.
- 评估这些人工智能工具在低收入和中等收入国家 (LMIC) 和高收入国家 (HIC) 的影响.
- 确定全球研究人员获得公平和可持续访问的障碍和促进因素,与世卫组织呼吁在健康领域安全和道德的人工智能相一致.
主要方法:
- 进行了范围审查,以识别和绘制AI工具和平台,以优化ES.
- 包括针对各种ES步骤开发和/或部署的AI工具的研究.
- 分析了137项研究的结果,记录了388个AI工具和平台.
主要成果:
- 在137项研究中确定了388个AI工具和平台,旨在优化证据合成.
- 记录了ES当前的人工智能格局,同时考虑LMIC和HIC.
- 强调需要解决全球公平和可持续获得这些工具的障碍和促进因素.
结论:
- 人工智能为提高证据综合的效率和降低证据综合的成本提供了巨大的潜力.
- 建议包括促进协作的人工智能平台,以获得公平的机会,建立人工智能方法的评估标准,并强调人力投入和多学科能力建设.
- 解决全球在获取和确保可信的人工智能实施方面的差异对于推动全球基于证据的决策至关重要.
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