Antarsih Novita Rina, Siregar Kemal Nazaruddin, Oktivasari Prihatin, Siswanto Bambang Budi
Doctoral Program, Faculty of Public Health, Universitas Indonesia, West Java, Indonesia; Department of Midwifery, Politeknik Kesehatan Kementrian Kesehatan Jakarta III, Jakarta, Indonesia.
Department of Biostatistics and Population, Faculty of Public Health, Universitas Indonesia, Depok, Indonesia.
Comput Biol Med. 2025 Sep;196(Pt B):110752. doi: 10.1016/j.compbiomed.2025.110752. Epub 2025 Jul 15.
Wearable technology has become increasingly essential in managing cardiovascular disease (CVD), offering innovative solutions for real-time monitoring and personalized care. Artificial intelligence (AI) is playing a growing role in enhancing the capabilities of wearable devices, yet the global research trends and knowledge gaps in this area remain underexplored. This study aims to provide a comprehensive bibliometric analysis of wearable technology research for CVD management, with a specific focus on the integration and impact of AI.
We conducted a bibliometric analysis of literature published between 2014 and 2024, sourced from major academic databases. The analysis employed citation, co-citation, and co-word mapping techniques using tools such as VOSviewer and Bibliometrix to identify key studies, emerging themes, and research gaps in wearable technology and AI for CVD management.
AI-powered wearables improve CVD diagnostics and patient outcomes, but challenges remain in clinical integration and data interoperability. These devices also play a crucial role in early atrial fibrillation (AF) detection, enhancing diagnostic accuracy and supporting timely medical interventions. AI-enhanced portable ECG technology further improves real-time decision-making in cardiovascular care, offering a transformative approach to personalized, evidence-based medicine.
AI integration in wearable technology is revolutionizing CVD management, offering precise, personalized care. However, challenges such as data security, algorithmic bias, and clinical validation persist. Ensuring privacy requires strong encryption and regulatory compliance. Large-scale trials, standardized data frameworks, and clinician training are essential to accelerate adoption, ensuring AI-powered wearables are effective, equitable, and sustainable in healthcare.
可穿戴技术在心血管疾病(CVD)管理中变得越来越重要,为实时监测和个性化护理提供了创新解决方案。人工智能(AI)在增强可穿戴设备功能方面发挥着越来越重要的作用,但该领域的全球研究趋势和知识差距仍未得到充分探索。本研究旨在对用于CVD管理的可穿戴技术研究进行全面的文献计量分析,特别关注AI的整合及其影响。
我们对2014年至2024年期间发表的文献进行了文献计量分析,文献来源为主要学术数据库。该分析采用了诸如VOSviewer和Bibliometrix等工具的引文、共引和共词映射技术,以识别可穿戴技术和用于CVD管理的AI方面的关键研究、新兴主题和研究差距。
人工智能驱动的可穿戴设备改善了CVD诊断和患者预后,但在临床整合和数据互操作性方面仍存在挑战。这些设备在早期心房颤动(AF)检测中也发挥着关键作用,提高了诊断准确性并支持及时的医疗干预。人工智能增强的便携式心电图技术进一步改善了心血管护理中的实时决策,为个性化、循证医学提供了一种变革性方法。
可穿戴技术中人工智能的整合正在彻底改变CVD管理,提供精确、个性化的护理。然而,数据安全、算法偏差和临床验证等挑战依然存在。确保隐私需要强大的加密和合规监管。大规模试验、标准化数据框架和临床医生培训对于加速采用至关重要,以确保人工智能驱动的可穿戴设备在医疗保健中有效、公平且可持续。