心脏病诊断中的可解释的人工智能:对机器学习,元启发式优化和冠状动脉疾病临床文本挖掘的系统审查
Majdi Jaradat1, Mohammed Awad2
1Department of Natural, Engineering and Technological Sciences, Arab American University (AAUP), Palestine.
International journal of medical informatics
|February 7, 2026
概括
人工智能 (AI),包括机器学习 (ML) 和自然语言处理 (NLP),显示出对诊断冠状动脉疾病 (CAD) 的希望. 需要进一步的研究来解决数据限制和临床验证,以便广泛采用.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 冠状动脉疾病 (CAD) 诊断依赖于先进的计算方法.
- 人工智能 (AI) 方法如机器学习 (ML),自然语言处理 (NLP),元启发式优化和可解释AI (XAI) 越来越多地被用于CAD预测和诊断.
研究的目的:
- 系统地审查和分析各种AI技术在预测和诊断CAD中的应用.
- 识别流行的人工智能模型,评估它们的性能,并评估可解释性和优化对临床实用性的影响.
主要方法:
- 在五个主要数据库 (PubMed,Scopus,IEEE Xplore,ACM数字图书馆,SpringerLink) 进行了系统的文献搜索,搜索了2022年1月至2025年8月期间发表的研究.
- 研究选择,数据提取和质量评估 (使用QUADAS-2) 是由双独立审查员进行的,遵循PRISMA指南.
- 收集的数据包括研究特征,AI模型类型,验证策略和性能指标.
主要成果:
- 包括61项研究,ML和深度学习模型在基准数据集上显示出高准确性,但临床验证有限.
- 基于变压器的NLP模型在医学文本分析中表现出有效性,但需要大量的数据和计算资源.
- 可解释性AI (XAI) 工具提高了模型透明度,尽管严格的评估仍然是一个挑战;元启发式算法提高了效率,但在临床叙述中未得到充分利用.
结论:
- 综合人工智能方法 (ML,NLP,优化,XAI) 为提高CAD诊断准确性和可解释性提供了显著的潜力.
- 临床采用受到数据稀缺,外部验证不足以及缺乏标准化,专注于临床医生的可解释性所阻碍.
- 未来的研究应该优先考虑在大型,多样化的现实数据集上验证的混合人工智能框架,以实现强大的临床集成.
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