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Updated: Sep 11, 2025
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
Evaluating a Customized Version of ChatGPT for Systematic Review Data Extraction in Health Research: Development and
Jayden Sercombe1, Zachary Bryant1, Jack Wilson1
1The Matilda Centre for Research in Mental Health and Substance Use, University of Sydney, Jane Foss Russell Building (G02), Level 6, Sydney, 2006, Australia, 612 8627 9380.
Background:
Systematic reviews are essential for synthesizing research in health sciences; however, they are resource-intensive and prone to human error. The data extraction phase, in which key details of studies are identified and recorded in a systematic manner, may benefit from the application of automation processes. Recent advancements in artificial intelligence, specifically in large language models (LLMs) such as ChatGPT, may streamline this process.
Objective:
This study aimed to develop and evaluate a custom Generative Pre-Training Transformer (GPT), named Systematic Review Extractor Pro, for automating the data extraction phase of systematic reviews in health research.
Methods:
OpenAI's GPT Builder was used to create a GPT tailored to extract information from academic manuscripts. The Role, Instruction, Steps, End goal, and Narrowing (RISEN) framework was used to inform prompt engineering for the GPT. A sample of 20 studies from two distinct systematic reviews was used to evaluate the GPT's performance in extraction. Agreement rates between the GPT outputs and human reviewers were calculated for each study subsection.
Results:
The mean time for human data extraction was 36 minutes per study, compared to 26.6 seconds for GPT generation, followed by 13 minutes of human review. The GPT demonstrated high overall agreement rates with human reviewers, achieving 91.45% for review 1 and 89.31% for review 2. It was particularly accurate in extracting study characteristics (review 1: 95.25%; review 2: 90.83%) and participant characteristics (review 1: 95.03%; review 2: 90.00%), with lower performance observed in more complex areas such as methodological characteristics (87.07%) and statistical results (77.50%). The GPT correctly extracted data in 14 instances (3.25% in review 1) and four instances (1.16% in review 2) when the human reviewer was incorrect.
Conclusions:
The custom GPT significantly reduced extraction time and shows evidence that it can extract data with high accuracy, particularly for participant and study characteristics. This tool may offer a viable option for researchers seeking to reduce resource demands during the extraction phase, although more research is needed to evaluate test-retest reliability, performance across broader review types, and accuracy in extracting statistical data. The tool developed in the current study has been made open access.
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