Elvas Luis B, Almeida Ana, Ferreira Joao C
Department of Logistics, Molde University College, Molde, Norway.
INESC INOV Rua Alves Redol, Lisbon, Portugal.
JMIR Med Inform. 2025 Mar 6;13:e64349. doi: 10.2196/64349.
Artificial intelligence (AI) has shown exponential growth and advancements, revolutionizing various fields, including health care. However, domain adaptation remains a significant challenge, as machine learning (ML) models often need to be applied across different health care settings with varying patient demographics and practices. This issue is critical for ensuring effective and equitable AI deployment. Cardiovascular diseases (CVDs), the leading cause of global mortality with 17.9 million annual deaths, encompass conditions like coronary heart disease and hypertension. The increasing availability of medical data, coupled with AI advancements, offers new opportunities for early detection and intervention in cardiovascular events, leveraging AI's capacity to analyze complex datasets and uncover critical patterns.
This review aims to examine AI methodologies combined with medical data to advance the intelligent monitoring and detection of CVDs, identifying areas for further research to enhance patient outcomes and support early interventions.
This review follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology to ensure a rigorous and transparent literature review process. This structured approach facilitated a comprehensive overview of the current state of research in this field.
Through the methodology used, 64 documents were retrieved, of which 40 documents met the inclusion criteria. The reviewed papers demonstrate advancements in AI and ML for CVD detection, classification, prediction, diagnosis, and patient monitoring. Techniques such as ensemble learning, deep neural networks, and feature selection improve prediction accuracy over traditional methods. ML models predict cardiovascular events and risks, with applications in monitoring via wearable technology. The integration of AI in health care supports early detection, personalized treatment, and risk assessment, possibly improving the management of CVDs.
The study concludes that AI and ML techniques can improve the accuracy of CVD classification, prediction, diagnosis, and monitoring. The integration of multiple data sources and noninvasive methods supports continuous monitoring and early detection. These advancements help enhance CVD management and patient outcomes, indicating the potential for AI to offer more precise and cost-effective solutions in health care.
人工智能（AI）呈现出指数级增长与进步，正在彻底改变包括医疗保健在内的各个领域。然而，领域适应仍然是一项重大挑战，因为机器学习（ML）模型常常需要应用于不同的医疗保健环境，这些环境中的患者人口统计学特征和医疗实践各不相同。这个问题对于确保人工智能的有效和公平部署至关重要。心血管疾病（CVD）是全球死亡的主要原因，每年有1790万人死亡，包括冠心病和高血压等病症。医疗数据的可用性不断提高，再加上人工智能的进步，为心血管事件的早期检测和干预提供了新机会，利用了人工智能分析复杂数据集和发现关键模式的能力。
本综述旨在研究结合医学数据的人工智能方法，以推进心血管疾病的智能监测和检测，确定进一步研究的领域，以改善患者预后并支持早期干预。
本综述遵循PRISMA（系统评价和荟萃分析的首选报告项目）方法，以确保严格和透明的文献综述过程。这种结构化方法有助于全面概述该领域的当前研究状况。
通过所使用的方法，检索到64篇文献，其中40篇符合纳入标准。综述的论文展示了人工智能和机器学习在心血管疾病检测、分类、预测、诊断和患者监测方面的进展。诸如集成学习、深度神经网络和特征选择等技术比传统方法提高了预测准确性。机器学习模型可预测心血管事件和风险，并应用于通过可穿戴技术进行监测。人工智能在医疗保健中的整合支持早期检测、个性化治疗和风险评估，可能改善心血管疾病的管理。
该研究得出结论，人工智能和机器学习技术可以提高心血管疾病分类、预测、诊断和监测的准确性。多数据源和非侵入性方法的整合支持持续监测和早期检测。这些进展有助于加强心血管疾病管理和改善患者预后，表明人工智能在医疗保健中提供更精确和更具成本效益解决方案的潜力。
