Mapping Research Trends and Collaborative Networks in Swarm Intelligence for Healthcare Through Visualization.

Area of Science:

• Artificial Intelligence
• Computational Biology
• Health Informatics

Background:

• Swarm intelligence, derived from social insect behavior, offers advanced AI solutions for complex, dynamic problems.
• The healthcare sector faces escalating data complexity and demands for optimized diagnostics and personalized treatments.

Purpose of the Study:

• To visualize and analyze the application of swarm intelligence in healthcare.
• To identify key research trends, influential contributors, and collaborative networks in this domain.
• To assess the potential of swarm intelligence in improving medical data management, diagnostics, and patient care.

Main Methods:

• Bibliometric analysis of Scopus literature (2003-2024) using Biblioshiny and VOSviewer.
• Identification of publication trends, central themes, prolific authors, and key journals.
• Mapping of collaborative networks and geographical contributions.

Main Results:

• A significant surge in swarm intelligence research in healthcare observed since 2017.
• Core themes include disease diagnosis, treatment optimization, medical image analysis, and real-time patient monitoring via Internet of Medical Things (IoMT) and swarm learning.
• China and India identified as leading contributors, highlighting a growing international research focus.
• Identification of key authors, journals, and collaborative networks shaping the field.

Conclusions:

• Swarm intelligence is a rapidly growing, multidisciplinary field with substantial potential in healthcare.
• Its application promises to enhance medical diagnostics, treatment strategies, and personalized patient care.
• Continued collaborative research is crucial for realizing the full impact of swarm intelligence in medicine.