EP109 Research trends and highlights toward artificial intelligence in pain: bibliometric analysis on Web of Science from 2014 to 2023
Bibliographic record
Abstract
Please confirm that an ethics committee approval has been applied for or granted: Not relevant Background and Aims This study aims to use bibliometric methods to identify the contribution of countries, journals, authors, research themes, and emerging trends in artificial intelligence (AI) in pain. Methods Articles on AI in pain were obtained from the Web of Science database which was accessed on 22 February 2024. TheVOSviewer program was used to visualize trends in research on artificial intelligence in pain. Results Analyses of 767 original articles revealed that the total number of publications has continually increased over the last 10 years. From 2014 to 2023, it was determined that there was an increase in the number of studies on the use of AI in pain [n:13(2014); n:240(2023)] (figure 1). Scientific Reports (n=31) and Journal of Clinical Medicine are the journals that published the most studies on the use of AI in pain (n=22). The countries with the highest number of studies are the United States (n=174), China (n=131), South Korea (n=88), Germany (n=72), Taiwan (n=59), England (n=54), Canada (n=43), Italy (n=41), Netherlands (n=36), India (n=35), Spain (n=34), Japan (n=33), Australia (n=21), Switzerland (n=24), Saudi Arabia (n=20). In the keyword co-occurrence analysis, 12 clusters were found; machine learning; spine, pain perception, pain, mhealth, pain management, blood sampling, epidural anesthesia, acute coronary syndrome, algorithmic approach, and pain assessment (figure 2). Conclusions The present study evaluated research on acupuncture for pain control using bibliometric methods and revealed current trends in artificial intelligence in pain research, as well as potential future hot spots of research in this field.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.098 | 0.152 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".