Application of Artificial Intelligence in Chronic Pain: Bibliometric Analysis

Area of Science:

• Bibliometric analysis
• Artificial Intelligence
• Chronic Pain Management

Background:

• Artificial intelligence (AI) shows significant promise in optimizing chronic pain (CP) treatment and resource allocation.
• Existing research on AI in CP is fragmented, lacking systematic analysis and a comprehensive overview.
• A knowledge map is needed to visualize the research landscape of AI applications in chronic pain.

Purpose of the Study:

• To conduct a bibliometric analysis of research at the intersection of AI and CP.
• To identify current research status, trends, and hotspots in AI for chronic pain management.
• To provide insights for researchers in the field of AI and chronic pain.

Main Methods:

• Data sourced from the Web of Science Core Collection up to October 2025.
• VOSviewer used for analyzing cooperation networks (countries, institutions, journals, authors) and keyword co-occurrence.
• CiteSpace employed to detect burst keywords and identify emerging research trends.

Main Results:

• Analysis of 356 studies from 54 countries and 882 institutions, published in 190 journals by 2,207 authors.
• Rapid publication growth observed from 2018 to 2025, with the United States leading in publications and citations.
• Key research clusters identified: chronic pain, machine learning, low back pain, and prediction; emerging trends include prediction, neural networks, pain management, and neck pain.

Conclusions:

• AI has made progress in chronic pain management, but challenges persist regarding international cooperation, model adaptability, and data privacy.
• Future research should prioritize international collaboration and interdisciplinary integration for practical AI application in clinical settings.
• Enhancing study reproducibility and scientific rigor is crucial for the wide applicability and clinical effectiveness of AI in chronic pain management.