Application of Artificial Intelligence in Chronic Pain: Bibliometric Analysis

Comprehensive Summary

The present study evaluates global research trends in the application of artificial intelligence broadly in understanding chronic pain. Researchers analyzed 356 articles from the Web of Science database between 1997 and 2025, using visualization tools (VOSviewer and CiteSpace) to map publication patterns, research output, and keyword trends. Hu et al. found that research on the intersection of AI and chronic pain saw a major upturn after 2018, with the United States leading in output and influence at 30% of all research output among the papers analyzed. The major research areas included machine learning-based prediction models, neuroimaging biomarker algorithms for diagnoses, AI-powered pain management tools, and pain-psychiatric comorbidity analyses. The keyword co-occurrence analysis map suggests that lower back pain, pain assessment, and chronic pain were among the popular topics studied in its relation to AI. Overall, the study demonstrates that AI in chronic pain is a rapidly expanding field with broad interdisciplinary stakeholders that spans from psychiatry to orthopedics. Despite the rapid growth, challenges remain in model generalizability, collaboration, and clinical implementation.

Outcomes and Implications

The research highlights the growing role of AI in advancing chronic pain research, particularly in improving prediction, diagnosis, and personalized management strategies. By identifying key research clusters, which included machine learning models, neuroimaging biomarkers, and digital health interventions, the study demonstrates how AI is being leveraged to enhance clinical decision-making and patient outcomes. The study also serves primarily to inform researchers on the dominant areas of focus within the field. Hu et al. identify hot topics at the intersection of AI and chronic pain, like AI-driven cognitive behavioral therapy (AI-CBT). Researchers are thus enabled to recognize gaps in the literature through a bibliometric assessment of the current literature available. For instance, while low back pain emerged as a highly represented keyword, less emphasis was placed on areas such as opioid-related pain or certain neuropathic pain conditions. Before AI can be broadly disseminated into clinical practice, standardized data, data security, reproducibility, multicenter validation, and interdisciplinary cooperation are needed in clinical research. In short, there is a large gap in our understanding of AI, its implementation, and the subsequent outcomes in chronic pain management.