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

## TL;DR

This paper analyzes how swarm intelligence is being used in healthcare, showing a growing trend in research and collaboration since 2017.

## Contribution

The study provides a visual analysis of swarm intelligence research trends and collaborative networks in healthcare using bibliometric tools.

## Key findings

- Publications on swarm intelligence in healthcare have increased significantly since 2017.
- Key themes include disease diagnosis, treatment optimization, and real-time patient monitoring.
- China and India are identified as major contributors to the field.

## Abstract

Swarm intelligence, evolved from the self-organized behavior of social insects, has become an essential method under artificial intelligence for handling complex and dynamic issues. This study visualizes and analyzes the use of swarm intelligence in healthcare, focusing on its role in managing rising medical data complexity, optimizing diagnostic and therapeutic solutions, and supporting personalized healthcare. The analysis, based on literature from Scopus (2003-2024) using Biblioshiny and VOSviewer, reveals a strong increase in publications since 2017, with central themes around disease diagnosis, treatment optimization, medical image analysis, and real-time patient monitoring through frameworks like the Internet of Medical Things (IoMT) and swarm learning. Key findings include the identification of prolific authors, influential journals, and significant collaborative networks, with China and India emerging as major contributors. These insights underscore the multidisciplinary nature of swarm intelligence in healthcare, positioning it as a potential game-changer in medical diagnostics and patient care through collaborative and innovative research.