AI Article Synopsis

  • The study reviews research on digital medicine in cardiovascular diseases over the past 20 years, focusing on development processes, knowledge structures, and research hotspots in the field.
  • A total of 5,265 articles were analyzed, revealing a steady increase in publications, with the U.S. leading in contributions, particularly from Harvard Medicine School and related institutions.
  • Future trends in digital medicine for CVD may include enhancing chronic care through digital interventions, leveraging machine learning for disease prediction and management, and utilizing wearable devices for real-time health monitoring.

Article Abstract

Objective: To review studies on digital medicine in cardiovascular diseases (CVD), discuss its development process, knowledge structure and research hotspots, and provide a perspective for researchers in this field.

Methods: The relevant literature in recent 20 years (January 2004 to October 2022) were retrieved from the Web of Science Core Collection (WoSCC). CiteSpace was used to demonstrate our knowledge of keywords, co-references and speculative frontiers. VOSviewer was used to chart the contributions of authors, institutions and countries and incorporates their link strength into the table.

Results: A total of 5265 English articles in set timespan were included. The number of publications increased steadily annually. The United States (US) produced the highest number of publications, followed by England. Most publications were from Harvard Medicine School, followed by Massachusetts General Hospital and Brigham Women's Hospital. The most authoritative academic journal was . Noseworthy PA may have the highest influence in this intersected field with the highest number of citations and total link strength. The utilization of wearable mobile devices in the context of CVD, encompassing the identification of risk factors, diagnosis and prevention of diseases, as well as early intervention and remote management of diseases, has been widely acknowledged as a knowledge base and an area of current interest. To investigate the impact of various digital medicine interventions on chronic care and assess their clinical effectiveness, examine the potential of machine learning (ML) in delivering clinical care for atrial fibrillation (AF) and identifying early disease risk factors, as well as explore the development of disease prediction models using neural networks (NNs), ML and unsupervised learning in CVD prognosis, may emerge as future trends and areas of focus.

Conclusion: Recently, there has been a significant surge of interest in the investigation of digital medicine in CVD. This initial bibliometric study offers a comprehensive analysis of the research landscape pertaining to digital medicine in CVD, thereby furnishing related scholars with a dependable reference to facilitate further progress in this domain.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10864893PMC
http://dx.doi.org/10.1016/j.heliyon.2024.e25318DOI Listing

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