评估人工智能对抗微生物药物管理的影响:与传统风险评分系统进行比较的元分析
Antonio Pinto1, Flavia Pennisi2, Giovanni Emanuele Ricciardi2
1Faculty of Medicine, University Vita-Salute San Raffaele, Milan, Italy.
Infectious diseases now
|May 16, 2025
概括
与传统方法相比,机器学习 (ML) 模型在抗菌药物管理 (AMS) 中显示出更好的灵敏度和负预测值. 然而,人们仍然担心人工智能应用中的通用性和公平差距.
科学领域:
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
- 公共卫生 公共卫生
背景情况:
- 抗微生物耐药性 (AMR) 需要先进的抗微生物管理 (AMS) 计划.
- 人工智能 (AI) 和机器学习 (ML) 提供了改善AMS决策的潜力.
研究的目的:
- 系统地审查和元分析AI在AMS中的影响.
- 将AMS中的AI/ML与传统风险评估系统的有效性进行比较.
主要方法:
- 在主要数据库 (PubMed/MEDLINE,Scopus,EMBASE,Web of Science) 进行全面搜索,截至2024年7月.
- 包括将AI/ML在AMS中与传统方法进行比较的研究.
- 使用科恩d的预测性能和诊断准确度指标的元分析,由QUADAS-AI评估偏差风险.
主要成果:
- 与传统方法相比,ML模型显示出更高的灵敏度和负预测值.
- 在应用随机效应模型时,在曲线下的面积,准确性,特异性或积极的预测值方面没有发现显著差异.
- 分析包括了27项研究,在最初识别的3,458项研究中.
结论:
- 机器学习工具显示出增强传统AMS策略的前景.
- 高异质性和不一致的模型结果引起了人们对概括性的担忧.
- 门诊和儿科设置的有限代表性表明在AMS的AI实施中存在关键的公平差距.
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