Related Experiment Video
Updated: Jan 12, 2026
05:47
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
1.3K
Comparing the accuracy of AI-assisted data extraction versus human double extraction in evidence synthesis: a
Zhen Peng1,2, Shiqi Fan3, Yuan Tian3
1Key Laboratory of Population Health Across Life Cycle, Ministry of Education of the People's Republic of China, Anhui Medical University, Hefei, Anhui, China.
BMJ Open
|November 6, 2025
Summary
This study compares an AI-human data extraction strategy against traditional human double extraction. The AI-human approach aims to improve efficiency and accuracy in systematic reviews.
Area of Science:
- Medical Informatics
- Systematic Review Methodology
Background:
- Traditional data extraction is time-consuming and labor-intensive.
- Artificial intelligence (AI) offers potential for efficient data extraction.
- AI is not yet a standalone solution for data extraction.
Purpose of the Study:
- To compare the efficiency and accuracy of an AI-human data extraction strategy versus human double extraction.
- To evaluate the performance of AI-assisted data extraction in systematic reviews.
Main Methods:
- A randomized controlled trial (RCT) with a 1:2 allocation ratio (AI group vs. non-AI group).
- The AI group uses AI extraction followed by human verification.
- The non-AI group uses human double extraction for event count and group size data from 10 sleep medicine RCTs.
Main Results:
- The primary outcome is the percentage of correct extractions for each data extraction task.
- Efficiency and accuracy metrics will be compared between the AI-human and human double extraction groups.
Conclusions:
- The study will determine if an AI-human hybrid approach is more efficient and accurate than traditional methods.
- Findings will inform best practices for data extraction in systematic reviews.
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