肺部疾病中的人工智能:当前的应用和下一步
Ferhan Karataş1, Öner Dikensoy1,2
1Department of Pulmonary Diseases, Koç University Hospital, İstanbul, Türkiye.
Thoracic research and practice
|January 15, 2026
概括
人工智能 (AI) 显著提高了部疾病的诊断,包括部溢血和恶性部间皮瘤. 人工智能模型显示高精度,可能会彻底改变诊断管理.
科学领域:
- 肺部病理学 肺部病理学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 胸腔疾病带来了诊断挑战和高医疗保健成本.
- 人工智能 (AI) 提供了更快,更准确的诊断的潜力.
- 本综述侧重于人工智能的应用,用于诊断多流,恶性多流,结核多,肺胸和恶性多间皮瘤.
研究的目的:
- 审查人工智能技术在诊断各种肺部疾病方面的现状.
- 评估AI在放射学,细胞学和综合诊断方法中的有效性.
- 确定AI对未来肺部疾病管理的潜在影响.
主要方法:
- 对深度学习算法的审查,以进行流的放射性诊断.
- 对人工智能模型进行分析,将临床参数 (CT,PET-CT,瘤标志物) 整合为排泄分类.
- 计算机辅助系统在细胞学诊断中的评估.
- 评估人工智能模型用于结核肺炎,肺胸炎和恶性肺间皮质瘤诊断.
主要成果:
- 深度学习显示了对膜溢出检测的高灵敏度和特异性.
- 整合临床数据的AI模型在区分良性和恶性输出方面达到高精度 (>0.90AUC).
- 人工智能在细胞学诊断方面的表现与专家相提并论,并且优于结核性肺炎的常规方法.
- 美国食品和药物管理局批准的AI模型有助于快速诊断肺胸病;AI有助于MPM预后评估和治疗监测.
结论:
- 人工智能显示出在一系列肺部疾病中提高诊断准确性和效率的巨大潜力.
- 进一步的大规模,多中心研究是必要的,以标准化和泛化AI模型.
- 人工智能已经准备好从根本上改变肺部疾病的诊断管理.
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