我们可以依靠人工智能来指导抗菌疗法吗? 一个系统的文献审查
Sulwan AlGain1,2, Alexandre R Marra3,4, Takaaki Kobayashi4,5
1King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia.
Antimicrobial stewardship & healthcare epidemiology : ASHE
|April 14, 2025
概括
人工智能 (AI) 通过改善抗菌素耐药性预测和抗生素处方,在传染病治疗方面表现有前途. 集成到临床决策支持系统 (CDSS) 的机器学习是有效的,但大型语言模型需要进一步验证临床使用.
科学领域:
- 传染性疾病 传染性疾病
- 人工智能的人工智能
- 临床决策支持 临床决策支持
背景情况:
- 人工智能 (AI) 具有改善传染病临床决策的潜力.
- 人工智能可以增强对抗菌素耐药性的预测,并优化抗生素处方.
- 这些技术旨在支持治疗策略并解决医疗保健缺口.
研究的目的:
- 评估人工智能的有效性,指导针对传染病的适当抗生素处方.
- 评估AI在抗菌药物管理和临床决策中的作用.
主要方法:
- 对人工智能 (机器学习,大型语言模型) 研究的系统文献审查,以指导抗生素处方.
- 搜索了主要的数据库 (PubMed,Embase,Scopus等) 在2024年10月25日之前.
- 包括评估AI在抗菌药物管理临床实践中的表现的研究.
主要成果:
- 17项研究在CDSS中使用机器学习,改善了对抗菌素耐药性的预测和使用.
- 对大型语言模型的六项研究表明,处方错误和安全风险更高.
- 大型语言模型需要精确的提示,以获得准确的临床指导.
结论:
- 在CDSS中的机器学习显示出增强临床决策和抗菌药物管理的前景.
- 目前,大型语言模型对于复杂的临床应用缺乏可靠性.
- 传染病专家仍然至关重要;人工智能在临床整合之前需要严格验证.
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