ArticleDigital health
Quantitative analysis of studies that use artificial intelligence on spinal diseases: A bibliometric analysis.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Artificial intelligence and large language models as a new threat to scientific truth - a call to action.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: This study aimed to evaluate the current research progress and future research directions of artificial intelligence in spinal diseases through a bibliometric analysis. Methods: Publications regarding spinal diseases and artificial intelligence published from 2006 to 2025 were extracted from the Web of Science Core Collection (WOSCC). Subsequently, a bibliometric analysis of these publications was conducted using CiteSpace, VOSviewer, and Bibliometrix Online Analysis Platform. Results: A total of 734 papers were included in the study. The annual publication numbers are on the rise. The USA (267 papers) and Beijing Jishuitan Hospital (24 papers) were identified as the most productive country and institution, respectively. Tian, Wei (25 papers) is the most productive author. "Spine" (49 publications) is the most productive journal. "Machine Learning" was the most cited keyword, with high-frequency keywords such as "Artificial Intelligence," "Robotic Surgery," "Deep learning," "Virtual Reality," and "predictive modeling" signaling hot topics in this field. Conclusions: There are increasingly many papers on artificial intelligence in spinal diseases. However, cooperation between institutions in various countries needs to be strengthened. In addition, this study summarized the research focus of artificial intelligence in spinal disorders as accurate diagnosis, robot-assisted surgery, and prognosis prediction, providing researchers with future research directions.
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.