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Using artificial intelligence tools to automate data extraction for living evidence syntheses.

Evan Mitchell1, Elisha B Are2, Caroline Colijn2

  • 1Department of Mathematics and Statistics, McMaster University, Hamilton, ON,Canada.

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Summary
This summary is machine-generated.

This study introduces an AI-powered Python program to automate data extraction for living evidence synthesis (LES). The tool significantly reduces manual effort in updating systematic reviews, enhancing research efficiency.

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Research
  • Epidemiology

Background:

  • Living evidence synthesis (LES) requires frequent updates of systematic reviews and meta-analyses.
  • Manual data extraction from new research articles is time-consuming and labor-intensive.
  • Current tools automate article retrieval but not data extraction.

Purpose of the Study:

  • To develop and test a Python program using AI (ChatGPT) to automate data extraction for LES.
  • To reduce the human time investment in data extraction without sacrificing accuracy.
  • To assess the utility of AI tools in scientific research workflows.

Main Methods:

  • A proof-of-concept Python program was developed.
  • The program utilizes ChatGPT to parse journal articles and extract relevant results.
  • The tool was tested on articles estimating the mean incubation period of COVID-19.

Main Results:

  • The AI-powered program successfully parsed articles and extracted data.
  • Significant reduction in human time investment for data extraction was observed.
  • Accuracy of extracted data was maintained.

Conclusions:

  • AI tools, like ChatGPT, can effectively automate data extraction for living evidence synthesis.
  • This approach can substantially improve the efficiency of systematic reviews and meta-analyses.
  • Further research is needed to address limitations regarding information processing rates for AI engines.