人工智能与传染病研究的定性或混合方法的交集:一个范围审查
Jane K Fieldhouse1, Jianye Ge1, Nistara Randhawa1
1Grand Challenges, University of California, Davis, United States.
Public health
|September 20, 2025
概括
人工智能 (AI) 和机器学习 (ML) 在传染病研究中增强了定性和混合方法. 人类监督对于准确,道德的AI整合到全球健康研究中至关重要.
科学领域:
- 公共卫生 公共卫生
- 传染病流行病学 传染病流行病学
- 计算社会科学 计算社会科学
背景情况:
- 人工智能 (AI) 和机器学习 (ML) 在全球卫生领域越来越多地被用于传染病研究.
- 社会科学研究对于了解人类在疾病预防和控制中的行为至关重要.
- 对AI在定性或混合方法研究中建立的最佳实践仍在发展.
研究的目的:
- 描述目前AI/ML应用在定性和混合方法研究中的现状,研究重点是传染病.
- 确定AI/ML方法如何与该领域的定性或混合方法集成.
- 评估使用AI/ML与定性或混合方法结合使用的主要原因.
主要方法:
- 进行了范围审查.
- 在2024年2月12日之前发表的研究,在PubMed,Scopus,Web of Science和Embase系统搜索.
- 包括的研究包括AI/ML方法与传染病研究的定性或混合方法.
主要成果:
- 在1342个记录中,有29个符合纳入标准.
- 人工智能/ML主要用于人工智能辅助的定性/混合方法分析,与人为主导的研究整合,以及人工智能辅助的工具开发.
- 大多数研究都分析了公共卫生紧急情况的数据,其中社交媒体数据分析是主要的应用.
- 人工智能/ML在处理定性数据方面表现出了效率,强调了对语境准确性的人类监督的必要性.
结论:
- 人工智能和机器学习技术可以显著提高传染病研究中的定性和混合方法的效率和范围.
- 最好的方法实践应该优先考虑人工智能与人为主导的定性分析的整合,以获得细微和伦理的发现.
- 确保这些方法在全球多样化的环境中,特别是低收入和中等收入国家的可访问性至关重要.
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