Using artificial intelligence for the development of a living evidence map: The pharmacopuncture example

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.