AI-powered stethoscopes offer a promising alternative for screening rheumatic heart disease (RHD), particularly in regions with limited diagnostic infrastructure. Early detection is vital, yet echocardiography, the gold standard tool, remains largely inaccessible in low-resource settings due to cost and workforce constraints. This review systematically examines machine learning (ML) applications from 2015 to 2025 that analyze electrocardiogram (ECG) and phonocardiogram (PCG) data to support accessible, scalable screening of all RHD variants in relation to the World Heart Federation's "25 by 25" goal to reduce RHD mortality. Using PRISMA-ScR guidelines, 37 peer-reviewed studies were selected from PubMed, IEEE Xplore, Scopus, and Embase. Convolutional neural networks (CNNs) dominate recent efforts, achieving a median accuracy of 97.75%, F1-score of 0.95, and AUROC of 0.89. However, challenges remain: 73% of studies used single-center datasets, 81.1% relied on private data, only 10.8% were externally validated, and none assessed cost-effectiveness. Although 45.9% originated from endemic regions, few addressed demographic diversity or implementation feasibility. These gaps underscore the disconnect between model performance and clinical readiness. Bridging this divide requires standardized benchmark datasets, prospective trials in endemic areas, and broader validation. If these issues are addressed, AI-augmented auscultation could transform cardiovascular diagnostics in underserved populations, thereby aiding early detection. This review also offers practical recommendations for building accessible ML-based RHD screening tools, aiming to close the diagnostic gap in low-resource settings where conventional auscultation may miss up to 90% of cases and echocardiography remains out of reach.

翻译：人工智能驱动的听诊器为风湿性心脏病筛查提供了一种前景广阔的替代方案，尤其在诊断基础设施有限的地区。早期检测至关重要，但作为金标准工具的超声心动图，由于成本和人力资源限制，在资源匮乏环境中仍难以普及。本综述系统性地审视了2015年至2025年间应用机器学习分析心电图与心音图数据以支持可及、可扩展的全类型RHD筛查的研究，这些研究与世界心脏联盟提出的降低RHD死亡率的"25 by 25"目标相关联。依据PRISMA-ScR指南，从PubMed、IEEE Xplore、Scopus和Embase数据库中筛选出37项同行评议研究。卷积神经网络主导了近期研究，取得了中位准确率97.75%、F1分数0.95和AUROC 0.89的性能。然而，挑战依然存在：73%的研究使用单中心数据集，81.1%依赖私有数据，仅10.8%经过外部验证，且无研究评估成本效益。尽管45.9%的研究源自流行地区，但极少关注人口多样性或实施可行性。这些差距凸显了模型性能与临床就绪度之间的脱节。弥合这一鸿沟需要标准化的基准数据集、在流行地区开展前瞻性试验以及更广泛的验证。若能解决这些问题，AI增强听诊技术有望变革服务不足人群的心血管诊断，从而助力早期检测。本综述还为构建可及的基于机器学习的RHD筛查工具提供了实用建议，旨在缩小资源匮乏地区的诊断差距——在这些地区，传统听诊可能漏诊高达90%的病例，而超声心动图仍遥不可及。