整合人工智能与对抗抗菌素耐药性的基因组测序:一个叙事性回顾
Giovanni Scaglione1, Nicolò Mastroianni2, Alberto Rizzo3
1Department of Infectious Diseases, Luigi Sacco Hospital, Milan, Italy.
Frontiers in public health
|February 16, 2026
概括
人工智能 (AI) 和机器学习 (ML) 可以增强基因组测序,用于诊断抗菌素耐药性 (AMR). 这些技术承诺更快,更准确的病原体识别和治疗指导.
科学领域:
- 微生物学 微生物学
- 基因组学就是基因组学.
- 人工智能的人工智能
背景情况:
- 抗菌素耐药性 (AMR) 是一个重大的全球卫生危机,需要先进的诊断工具.
- 基因组测序提供了详细的病原体洞察力,但面临着成本和时间等临床采用障碍.
研究的目的:
- 审查AI和ML如何改善AMR的基于测序的诊断.
- 探索人工智能在基因组和非基因组方法中用于病原体表征的应用.
主要方法:
- 在全基因组测序和元基因组学中AI/ML应用的叙述性综述.
- 评估人工智能与拉曼光谱学和MALDI-TOF MS.的整合.
- 讨论人工智能引导诊断的挑战和未来方向.
主要成果:
- 人工智能增强了AMR预测,泛基因组分析和使用基因组数据检测疫情.
- 人工智能驱动的元基因组学有助于病原体和抗体的分析.
- 与MALDI-TOF MS等快速方法的ML集成显示了快速电阻分析的潜力.
结论:
- 人工智能和机器学习可以克服AMR当前测序诊断的局限性.
- 整合人工智能与基因组和快速表型工具是实时监测和抗菌药物管理的关键.
- 解决实际障碍对于可扩展,可解释和公平的AI诊断至关重要.
关键词:
抗微生物耐药性 抗微生物耐药性人工智能是一种人工智能.诊断 诊断 诊断 诊断 诊断 诊断基因组测序是指对基因组进行测序.感染的感染感染.机器学习是机器学习.监控监督监督监督监督监督监督监督监督监督监督监督监督监督监督监督监督监督更多相关视频
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