人工智能驱动的慢性阻塞性肺病检测技术-一篇评论
Abdul Rahaman Wahab Sait1,2, Mujeeb Ahmed Shaikh2,3
1Department of Archives and Communication, Center of Documentation and Administrative Communication, King Faisal University, P.O. Box 400, Hofuf 31982, Saudi Arabia.
Diagnostics (Basel, Switzerland)
|October 29, 2025
概括
人工智能 (AI) 可以通过使用各种数据提高慢性阻塞性肺病 (COPD) 诊断. 本综述综合了用于COPD检测的AI框架,确定局限性并提出未来研究方向,以提高概括性和公平性.
科学领域:
- 呼吸系统医学 呼吸系统医学
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 慢性阻塞性肺病 (COPD) 是全球主要的健康负担,传统的诊断需要专门的资源.
- 人工智能 (AI) 通过整合各种数据模式,为改善COPD诊断提供了潜力.
- 现有的审查通常集中在单个数据类型上,缺乏对COPD人工智能模型解释性和解释性的洞察力.
研究的目的:
- 系统地审查用于COPD识别的AI驱动框架.
- 在AI驱动的COPD诊断中分析数据模式,方法创新和评估策略.
- 为了确定报告的局限性,潜在的偏见,以及人工智能辅助的COPD检测的未来研究需求.
主要方法:
- 系统的文献搜索,遵循系统审查和元分析 (PRISMA) 准则的首选报告项目.
- 从1978年记录的初始数据库中包括22项研究.
- 分析重点是数据模式,人工智能方法,绩效评估和研究局限性.
主要成果:
- 人工智能框架在特定的COPD检测环境中显示出高性能.
- 大多数审查的研究都是回顾性的,缺乏多样性和外部/前性验证.
- 在目前的COPD人工智能模型中,发现了关于概括性和潜在偏差的重大局限性.
结论:
- 人工智能有望提高COPD诊断,但目前的研究面临普遍性挑战.
- 人工智能模型需要前性,多中心和多民族的验证.
- 未来的研究应该优先开发公平和广泛适用的COPD人工智能辅助诊断工具.
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