人工智能在传染病和抗菌素耐药性的挑战和应用
Angela Cesaro1,2,3,4, Samuel C Hoffman5, Payel Das6
1Machine Biology Group, Department of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
npj antimicrobials and resistance
|January 22, 2025
概括
人工智能 (AI) 通过实现更快的诊断和新型抗生素发现,彻底改变了传染病控制. 人工智能工具增强了病原体检测,预测了耐药性,并加速了新抗菌化合物的识别.
科学领域:
- 计算生物学是一种计算生物学.
- 传染病流行病学 传染病流行病学
- 药理学 药理学是指药理学的学科.
背景情况:
- 传统的传染病诊断往往需要长时间的等待.
- 在病原体检测和抗生素开发中急需加速方法.
- 抗微生物耐药性需要不断发现新的治疗药物.
研究的目的:
- 审查人工智能 (AI) 在传染病控制中的变革性应用.
- 突出AI在提高诊断速度和准确度方面的作用.
- 探索AI对发现和开发新抗生素的贡献.
主要方法:
- 关于人工智能在传染病诊断和治疗中的应用现有文献的综述.
- 分析用于病原体检测和药物发现的机器学习和深度学习方法.
- 评估人工智能对抗生素管理和抗菌化合物的识别的影响.
主要成果:
- 人工智能显著提高了病原体检测和诊断的速度和准确性.
- 机器学习和深度学习模型可以有效预测抗菌素耐药性模式.
- 人工智能加速了新型治疗化合物的发现,包括抗微生物和小分子.
结论:
- 人工智能在管理传染病方面带来了强大的范式转变.
- 人工智能应用在诊断,治疗和抗生素发现管道方面提供了实质性的改进.
- 需要进一步的研究和开发,才能充分发挥AI在打击传染病和抗菌素耐药性的潜力.
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