临床微生物学和人工智能:不同的应用,挑战和未来前景
Wafaa S Khalaf1, Radwa N Morgan2, Walid F Elkhatib3
1Department of Microbiology and Immunology, Faculty of Pharmacy (Girls), Al-Azhar University, Nasr city, Cairo 11751, Egypt.
Journal of microbiological methods
|April 6, 2025
概括
人工智能 (AI) 通过改进数据分析以获得更快,更便宜的方法来增强临床微生物学. 人工智能算法有助于开发新的抗微生物药物和疫苗,尽管道德和数据挑战仍然存在.
科学领域:
- 临床微生物学 临床微生物学
- 生物信息学是一种生物信息学.
- 人工智能的人工智能
背景情况:
- 传统的临床微生物学方法耗时且昂贵.
- 人工智能 (AI) 提供先进的数据处理和分析能力.
- 机器学习 (ML) 和深度学习 (DL) 算法在微生物学中越来越多地被使用.
研究的目的:
- 审查最近在临床微生物学中的人工智能应用.
- 为了教育临床从业人员在ML算法实施.
- 讨论人工智能在传染病流行病学中的挑战和未来机遇.
主要方法:
- 利用各种微生物学数据集,包括光谱分析,显微镜图像和基因组/蛋白质序列.
- 培训和评估各种AI,ML和DL算法.
- 整合AI与定量结构-活性关系 (QSAR) 模型用于抗微生物发现.
主要成果:
- 与传统方法相比,AI算法最大限度地减少了时间,精力和成本.
- 人工智能在疫苗开发和发现抗病毒药物的过程中起到了至关重要的作用,包括通过药物重新定位.
- 人工智能应用包括光谱分析,图像分析和序列数据分析.
结论:
- 人工智能通过提高效率并使新的发现成为可能,显著地有利于临床微生物学.
- 为了成功实施人工智能,需要解决诸如道德考虑,数据偏见和训练错误等挑战.
- 人工智能在传染病流行病学和抗菌素耐药性管理方面有未来的机会.
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