Global trends and hotspots of artificial intelligence in pain management: A bibliometric analysis

Objective To systematically explore international research trends and dynamics in the interdisciplinary field of artificial intelligence (AI) and pain management over the past decade (2016-2025) and delineate current research frontiers. Methods Publications were retrieved from Web of Science Core Collection database and subjected to bibliometric analysis. VOSviewer, Scimago Graphica, and other tools were used for bibliometric analysis and visualization. Results A total of 1022 articles were included. Publication output showed an exponential upward trend overall, despite a temporary slowdown in 2022, followed by accelerated growth from 2023 onward. The United States led in global publication volume and served as the core hub of international collaborative networks, followed by China. In terms of journals, Pain published the highest number of relevant studies. Regarding authorship, Cao Rui, Wang Bin, and David Borsook emerged as the most influential scholars. Reference and keyword analyses delineate a clear evolution: early research established foundational insights into mechanisms. Mid-period work integrated AI and machine learning with multimodal tools like functional magnetic resonance imaging (fMRI) for objective assessment; recent advances have shifted toward clinical translation, focusing on precision medicine and the refinement of pain classification and assessment frameworks. Conclusion This study maps the evolutionary trajectory of AI research in pain management over the past decade. Future efforts should prioritize strengthening international collaboration, promoting large-scale clinical validation of AI tools, standardizing data sharing, and addressing equity in access to technology to meet unmet clinical needs in pain care.