Naser Ahmad Moayad, Vyas Rhea, Morgan Ahmed Ashraf, Kalaiger Abdul Mukhtadir, Kharawala Amrin, Nagraj Sanjana, Agarwal Raksheeth, Maliha Maisha, Mangeshkar Shaunak, Singh Nikita, Satish Vikyath, Mathai Sheetal, Palaiodimos Leonidas, Faillace Robert T
Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, New York, NY 10461, USA.
Department of Medicine, Montefiore Wakefield Medical Center, New York, NY 10461, USA.
Diagnostics (Basel). 2025 Apr 1;15(7):889. doi: 10.3390/diagnostics15070889.
Pulmonary embolism (PE) remains a critical condition with significant mortality and morbidity, necessitating timely detection and intervention to improve patient outcomes. This review examines the evolving role of artificial intelligence (AI) in PE management. Two primary AI-driven models that are currently being explored are deep convolutional neural networks (DCNNs) for enhanced image-based detection and natural language processing (NLP) for improved risk stratification using electronic health records. A major advancement in this field was the FDA approval of the Aidoc© AI model, which has demonstrated high specificity and negative predictive value in PE diagnosis from imaging scans. Additionally, AI is being explored for optimizing anticoagulation strategies and predicting PE recurrence risk. While further large-scale studies are needed to fully establish AI's role in clinical practice, its integration holds significant potential to enhance diagnostic accuracy and overall patient management.
肺栓塞（PE）仍然是一种危急病症，具有较高的死亡率和发病率，需要及时检测和干预以改善患者预后。本综述探讨了人工智能（AI）在PE管理中不断演变的作用。目前正在探索的两种主要的人工智能驱动模型是用于增强基于图像检测的深度卷积神经网络（DCNN）和用于利用电子健康记录改善风险分层的自然语言处理（NLP）。该领域的一项重大进展是美国食品药品监督管理局（FDA）批准了Aidoc©人工智能模型，该模型在通过影像学扫描进行PE诊断时已显示出高特异性和阴性预测值。此外，正在探索利用人工智能优化抗凝策略并预测PE复发风险。虽然需要进一步的大规模研究来全面确立人工智能在临床实践中的作用，但其整合具有显著潜力，可提高诊断准确性和整体患者管理水平。
