SynthesisFrontiers in medicine2025
Two-decade dialogue between artificial intelligence and osteoporosis: research trajectories and frontier projections under bibliometric and visual analysis.
Synthesis in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Artificial Intelligence-Driven Precision Bisphosphonate Therapy in Osteoporosis: Mechanisms, Clinical Applications, Risks, and Future Directions.Orthopaedic surgery · 2026Review
- Intelligent technologies in operating room nursing: A bibliometric analysis of research.International journal of nursing studies advances · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.