人工智能,机器学习和深度学习:感染临床医生的潜在资源
Anastasia A Theodosiou1, Robert C Read1
1Clinical and Experimental Sciences and NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Tremona Road, SO166YD Southampton, United Kingdom.
The Journal of infection
|July 19, 2023
概括
人工智能 (AI) 在感染研究和管理方面表现有前途,在诊断和疫情预测方面有新兴应用. 然而,现实世界的临床实用性仍然有限,强调需要进一步验证和道德考虑.
科学领域:
- 传染性疾病 传染性疾病
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
背景情况:
- 人工智能 (AI),机器学习和深度学习越来越多地被用于人类感染研究和管理.
- 生成型人工智能也在被调查,以寻找其在这个领域的潜在应用.
研究的目的:
- 总结AI在临床感染实践中的近期和潜在的未来应用.
- 评估人工智能工具对管理人类感染的相关性.
主要方法:
- 对1617个PubMed结果进行了全面的文献搜索,优先考虑临床试验,系统性审查和元分析.
- 审查的重点是利用前性收集的现实世界数据进行临床验证和具有翻译潜力的研究.
主要成果:
- 人工智能在实验室诊断 (例如,抗菌素耐药性分析),临床成像 (例如,结核病诊断) 和临床决策支持 (例如,败血症预测) 中显示出潜力.
- 目前的研究往往缺乏现实世界的验证和临床实用性指标,在设计和报告中存在显著的异质性.
- 需要解决实际和伦理方面的挑战,包括算法透明度和偏见.
结论:
- 虽然用于感染研究的AI开发正在迅速推进,但其当前的现实世界临床实用性是温和的.
- 进一步的研究和验证对于实现AI在临床感染实践中的全部潜力至关重要.
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