Two-decade dialogue between artificial intelligence and osteoporosis: research trajectories and frontier projections under bibliometric and visual analysis.

PURPOSE: This study systematically evaluated the intellectual progress in artificial intelligence (AI)-driven osteoporosis research between 2004 and 2024 by employing scientometric and visualization techniques. Through mapping knowledge domains and identifying emerging trends, it offered actionable recommendations and strategic insights to guide future scholarly endeavors.

METHODS

We queried the Web of Science Core Collection for English-language articles and reviews published between January 1, 2004, and November 30, 2024, using search terms including "osteoporosis," "deep learning," "convolutional neural networks," and "artificial intelligence." Bibliometric data were processed via VOSviewer (v1.6.20), CiteSpace (v6.3.R1), Scimago Graphica (v 1.0.46), and the R package Bibliometrix to quantify annual publication trends, assess national/institutional contributions, evaluate journal/author impact metrics, and map keyword co-occurrence and burst dynamics.

RESULTS

The bibliometric analysis identified 408 publications (343 articles, 65 reviews) from 2004 to 2024, with a marked increase in output observed in 2024. China and the United States dominated scholarly productivity and citation impact. Leading institutions included the Technical University of Munich and Seoul National University, while Osteoporosis International emerged as the most influential journal. Prolific authors such as Thomas Baum demonstrated significant academic leadership. Keyword co-occurrence analysis revealed deep learning, artificial intelligence, and diagnosis as core research frontiers, signaling future technological and clinical priorities.

CONCLUSION

This study represents the first comprehensive bibliometric analysis of research on artificial intelligence in the field of osteoporosis. It not only outlines the field's development trajectory and emerging frontiers but also highlights the research focus on AI technologies, particularly deep learning. Furthermore, it emphasizes critical challenges in clinical translation, such as algorithm optimization, model interpretability, and ethical privacy concerns. By systematically identifying key contributors, collaborative networks, and evolving research fronts, this study provides a foundational roadmap for the field. It offers strategic priorities for researchers to address methodological gaps, serves as a reference for clinicians to understand the evolving technological toolkit, and provides a basis for policymakers to promote interdisciplinary collaboration.