Application of artificial intelligence in the health management of chronic disease: bibliometric analysis DOI Creative Commons
Minggui Pan, Rong Li, Junfan Wei

et al.

Frontiers in Medicine, Journal Year: 2025, Volume and Issue: 11

Published: Jan. 7, 2025

With the rising global burden of chronic diseases, traditional health management models are encountering significant challenges. The integration artificial intelligence (AI) into disease has enhanced patient care efficiency, optimized treatment strategies, and reduced healthcare costs, providing innovative solutions in this field. However, current research remains fragmented lacks systematic, comprehensive analysis. This study conducts a bibliometric analysis AI applications management, aiming to identify trends, highlight key areas, provide valuable insights state Hoping our findings will serve as useful reference for guiding further fostering effective application healthcare. Web Science Core Collection database was utilized source. All relevant publications from inception August 2024 were retrieved. external characteristics summarized using HistCite. Keyword co-occurrences among countries, authors, institutions analyzed with Vosviewer, while CiteSpace employed assess keyword frequencies trends. A total 341 retrieved, originating 775 across 55 published 175 journals by 2,128 authors. notable surge occurred between 2013 2024, accounting 95.31% (325/341) output. United States Journal Medical Internet Research leading contributors Our revealed four primary clusters: diagnosis, care, telemedicine, technology. Recent trends indicate that mobile technologies machine learning have emerged focal points field management. Despite advancements several critical challenges persist. These include improving quality, greater international inter-institutional collaboration, standardizing data-sharing practices, addressing ethical legal concerns. Future should prioritize strengthening partnerships facilitate cross-disciplinary cross-regional knowledge exchange, optimizing more precise ensuring their seamless clinical practice.

Language: Английский

Application of artificial intelligence in the health management of chronic disease: bibliometric analysis DOI Creative Commons
Minggui Pan, Rong Li, Junfan Wei

et al.

Frontiers in Medicine, Journal Year: 2025, Volume and Issue: 11

Published: Jan. 7, 2025

With the rising global burden of chronic diseases, traditional health management models are encountering significant challenges. The integration artificial intelligence (AI) into disease has enhanced patient care efficiency, optimized treatment strategies, and reduced healthcare costs, providing innovative solutions in this field. However, current research remains fragmented lacks systematic, comprehensive analysis. This study conducts a bibliometric analysis AI applications management, aiming to identify trends, highlight key areas, provide valuable insights state Hoping our findings will serve as useful reference for guiding further fostering effective application healthcare. Web Science Core Collection database was utilized source. All relevant publications from inception August 2024 were retrieved. external characteristics summarized using HistCite. Keyword co-occurrences among countries, authors, institutions analyzed with Vosviewer, while CiteSpace employed assess keyword frequencies trends. A total 341 retrieved, originating 775 across 55 published 175 journals by 2,128 authors. notable surge occurred between 2013 2024, accounting 95.31% (325/341) output. United States Journal Medical Internet Research leading contributors Our revealed four primary clusters: diagnosis, care, telemedicine, technology. Recent trends indicate that mobile technologies machine learning have emerged focal points field management. Despite advancements several critical challenges persist. These include improving quality, greater international inter-institutional collaboration, standardizing data-sharing practices, addressing ethical legal concerns. Future should prioritize strengthening partnerships facilitate cross-disciplinary cross-regional knowledge exchange, optimizing more precise ensuring their seamless clinical practice.

Language: Английский

Citations

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