ArticleIntegrative medicine research2025
Using artificial intelligence for the development of a living evidence map: The pharmacopuncture example.
Article in Integrative medicine research, 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.
- AI in medicine and traditional medicine - opportunities for healthcare transformation.Integrative medicine research · 2025Article
- Artificial intelligence in integrative medicine: Transforming education for a digital future.Integrative medicine research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Evidence map is a tool that visualizes the research status to identify research gaps and set priorities, but it has the limitation of the burden of continuous literature monitoring. Pharmacopuncture is a therapeutic modality used in Korean medicine that involves the injection of medicinal extracts into acupoints. This study aimed to develop an artificial intelligence (AI)-based automated system for building and maintaining a living evidence map in the field of pharmacopuncture research and verify its performance. Methods: A web-based system that automates literature search, selection, data extraction, and classification using PubMed API and Gemini AI was developed. The accuracy of nine tasks was evaluated and time efficiency was measured using manual review by experts as a standard reference. A visualization system using interactive bubble charts was implemented to provide a research gap identification function. Results: The AI system achieved an overall accuracy of 94.00% (error rate of 6.00%) for 202 articles, including detailed data extraction for 90 articles. Task-specific performance varied from sample size extraction (0% error rate) to pharmacopuncture name extraction (22.22% error rate), with high accuracy of over 90% in most tasks. Time efficiency was improved by 68.9% (190 vs. 59 minutes, including quality control), demonstrating that daily updates are practically feasible. Conclusions: The developed visualization system significantly improves the existing static evidence organization method by intuitively identifying research gaps. The AI-based living evidence map enables continuous evidence monitoring in the field of pharmacopuncture research with high accuracy and significant time savings.
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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.