用机器学习指导抗生素治疗:CDSS在细菌病管理中的现实应用
Juan Carlos Gómez de la Torre1,2,3, Ari Frenkel2, Carlos Chavez-Lencinas4,5
1Clinical Laboratory Roe, Lima 15076, Peru.
Life (Basel, Switzerland)
|November 27, 2025
概括
由人工智能 (AI) 驱动的临床决策支持系统 (CDSS) 显著帮助专业医生管理细菌病例. 人工智能建议和医生判断之间的高度一致性表明,人工智能可以优化抗生素管理和临床决策.
科学领域:
- 传染性疾病 传染性疾病
- 医疗信息学 医疗信息学
- 临床决策支持 临床决策支持
背景情况:
- 细菌病是一个危急的病情,死亡率很高,经常因延迟抗生素治疗而恶化.
- 有效的抗微生物药物管理对于改善细菌病管理中的患者结果至关重要.
- 对传染病专家的访问有限可能会阻碍最佳治疗决策.
研究的目的:
- 评估人工智能驱动的CDSS (OneChoice和OneChoice融合) 在管理细菌病的临床实用性.
- 评估人工智能建议对专家医生的临床判断和决策过程的影响.
- 确定人工智能产生的建议与医生主导的治疗选择之间的一致性.
主要方法:
- 对65名专科医生进行了横截面调查.
- 医生们通过AI-CDSS建议审查了细菌病例的临床细节.
- 参与者评估了AI的有用性,对判断的影响,以及与他们自己的建议的一致性.
主要成果:
- 97.8%的医生发现人工智能有助于在细菌病管理中的决策.
- 在AI建议和医生判断之间观察到实质性的一致性 (87.8%).
- 人工智能引导的治疗修改发生在68.9%的病例中,这表明了显著的临床影响.
结论:
- 人工智能驱动的CDSS有效地支持临床判断和细菌病中的抗菌药物管理.
- 这些系统可以弥合专业知识的差距,特别是在传染病专家稀缺的地方.
- 人工智能建议应该增强,而不是取代临床判断,特别是在复杂的情况下.
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