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人工智能在临床诊断中用于早期检测慢性疾病:系统性审查

E Manzhalii¹, Y Dekhtiar², V Bannikov³

¹1Doctor of Medical Science, Professor, Professor of the Department of Propedeutics of Internal Medicine at the Bogomolets Medical University, Kyiv, Ukraine.

Georgian medical news

|March 10, 2026

概括

人工智能 (AI) 显示出早期慢性疾病检测的前景,混合模型在准确性方面表现出色. 需要进一步的研究来解决数据异质性,并提高临床使用的概括性.

科学领域:

医疗信息学医疗信息学
医疗保健中的人工智能
慢性疾病管理慢性疾病管理

背景情况:

早期发现慢性疾病对于减少患者发病率和医疗保健系统负担至关重要.
人工智能 (AI) 为改善诊断准确性,风险预测和临床决策提供了巨大的潜力.
本综述综合了当前关于人工智能应用在诊断各种慢性疾病方面的证据.

研究的目的:

系统地审查和综合人工智能驱动的慢性疾病诊断系统的最新证据.
评估AI模型在慢性疾病检测中的性能和方法.
确定AI在临床实践中实施的挑战和未来方向.

主要方法:

在遵守PRISMA指南的基础上进行系统审查.
搜索了主要的数据库 (PubMed/MEDLINE,Scopus,Web of Science,IEEE Xplore,Embase) 来查找从2020年1月到2025年11月的研究.
包括来自13个国家的32项研究,重点关注各种慢性疾病.

主要成果:

混合人工智能模型 (56%) 是最常见的,特别是代谢疾病,其次是机器学习 (25%) 和深度学习 (19%).
研究涵盖了代谢/心脏代谢 (20),肌肉骨 (3),肺 (3),癌症/血液 (3),神经退行性 (1),眼科/牙科 (2) 疾病.
人工智能模型显示出高预测性能,AUC (0.7467-1.0),准确度 (77.08%-99.97%),灵敏度 (77%-100%) 和特异性 (59.2%-100%).

结论:

混合人工智能模型显示了使用多模式数据 (实验室,临床,成像) 早期发现慢性疾病的巨大潜力.
挑战包括数据集异质性,回顾性设计,有限的外部验证和不一致的报告,影响概括性.
未来的多中心试验,标准化数据集和增强的方法透明度对于临床实施至关重要.

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