人工智能在慢性阻塞性肺病中的应用:对诊断,基于症状和结果预测方法的全球范围审查
Alberto Pinheira1,2,3, Manuel Casal-Guisande1,2,4, Cristina Represas-Represas2,4,5
1Department of Design in Engineering, University of Vigo, 36208 Vigo, Spain.
Biomedicines
|December 30, 2025
概括
人工智能 (AI) 在诊断和管理慢性阻塞性肺病 (COPD) 中表现有前途. 然而,大多数用于COPD的AI模型需要进一步验证和临床实施才能广泛使用.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 呼吸系统医学 呼吸系统医学
背景情况:
- 慢性阻塞性肺病 (COPD) 构成一个重大的全球健康挑战,需要先进的诊断和管理策略.
- 人工智能 (AI) 通过分析复杂的健康数据,为改善临床决策和COPD患者的治疗结果提供了潜在的解决方案.
研究的目的:
- 系统地绘制和分类现有关于COPDAI应用的研究.
- 在三个关键领域识别AI应用:诊断,临床症状和临床结果.
主要方法:
- 根据阿克西和奥马利框架进行范围审查.
- 使用人口-概念-上下文 (PCC) 标准进行全面的数据库搜索 (PubMed,Scopus,IEEE Xplore,Google Scholar).
- 包括英语原创研究文章,没有地理限制.
主要成果:
- 包括120项研究,其中大部分数据来自亚洲和欧洲;有限的数据来自南美和非洲.
- 主要的人工智能技术包括深度学习 (DL),特别是用于成像的卷积神经网络 (CNN),以及用于临床数据的基于树的机器学习 (ML).
- 人工智能在诊断方面表现出很高的准确性,在症状分析方面有新兴的研究;在未来的验证和临床实施方面存在差距.
结论:
- 人工智能证明了COPD诊断,结果预测和症状分析的潜力,但模型通常是不成熟的.
- 方法上的局限性和外部验证不足阻碍了人工智能工具转化为常规的临床实践.
- 未来的研究应该专注于临床验证,可解释的AI和标准化的数据集,以便可靠地将AI纳入COPD护理.
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