Meng Lichong, Lian Kun, Zhang Junyu, Li Lin, Hu Zhixi
School of Traditional Chinese Medicine, Hunan University of Chinese Medicine, Changsha, Hunan, 410208, People's Republic of China.
Provincial Key Laboratory of TCM Diagnostics, Hunan University of Chinese Medicine, Changsha, Hunan, 410208, People's Republic of China.
J Multidiscip Healthc. 2025 May 26;18:2941-2956. doi: 10.2147/JMDH.S525739. eCollection 2025.
To investigate the role of artificial intelligence in enhancing precise diagnosis, personalized treatment, and efficient monitoring of heart failure over the past two decades and to predict future advancements of these investigations.
A literature search was conducted using keywords from the Web of Science database from January 1, 2004, to August 31, 2024, and 684 articles were retrieved. Bibliometric and visual analysis was conducted to examine annual publication volume; and to analyze authors, institutions, countries, journals, references, and keywords. The following tools were utilized for the analysis: Citespace, SCImago Graphica, Microsoft Office Excel, VOSviewer, and Pajek.
The 684 retrieved studies comprised 70 countries/regions, 1550 institutions, and 4610 authors. The annual publishing output increased gradually between 2004 and 2016, and escalated significantly beyond 2017, particularly from 2021 to 2024. This upward trend is anticipated to persist in the future. Sengupta, Partho P., and Shah, Sanjiv J. were the most productive authors. The University of California and Harvard University were the leading institutions in the number of publications within this discipline. The primary nations conducting research in this domain are China and the United States; the United States predominates research impact and global collaboration. Moreover, Frontiers in Cardiovascular Medicine is the leading journal with the most articles published in this area, while Circulation ranks the highest in co-citations. The keywords include HF, machine learning, AI, and diagnosis.
The application of AI in HF is a global concern in research. Currently, investigations address AI-enhanced HF diagnosis and risk assessment; AI-powered personalized treatment strategies, remote patient monitoring, multi-omics data integration, and HF mechanisms. Predictably, optimizing the use of AI in the ICU and Multimodal data are future trends in research, with AI substantially facilitating effective management of HF.
探讨人工智能在过去二十年中对心力衰竭精确诊断、个性化治疗及高效监测方面的作用，并预测这些研究的未来进展。
使用来自科学网数据库2004年1月1日至2024年8月31日的关键词进行文献检索，共检索到684篇文章。进行文献计量和可视化分析以检查年度发表量；并分析作者、机构、国家、期刊、参考文献和关键词。分析使用了以下工具：Citespace、SCImago Graphica、Microsoft Office Excel、VOSviewer和Pajek。
检索到的684项研究涉及70个国家/地区、1550个机构和4610位作者。2004年至2016年间年度发表量逐渐增加，2017年以后显著上升，特别是2021年至2024年。预计这一上升趋势未来将持续。Partho P. Sengupta和Sanjiv J. Shah是发文量最多的作者。加利福尼亚大学和哈佛大学是该学科领域发表论文数量最多的领先机构。开展该领域研究的主要国家是中国和美国；美国在研究影响力和全球合作方面占主导地位。此外，《心血管医学前沿》是该领域发表文章最多的领先期刊，而《循环》在共被引频次方面排名最高。关键词包括心力衰竭、机器学习、人工智能和诊断。
人工智能在心力衰竭中的应用是全球研究关注的焦点。目前，研究涉及人工智能增强的心力衰竭诊断和风险评估；人工智能驱动的个性化治疗策略、远程患者监测、多组学数据整合以及心力衰竭机制。可以预见，优化人工智能在重症监护病房的应用以及多模态数据是未来研究的趋势，人工智能将极大地促进心力衰竭的有效管理。
