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Generative artificial intelligence (GenAI) shows significant inaccuracies in evidence synthesis tasks like searching and screening. Current evidence does not support its use without human oversight, though it may assist with specific tasks.

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Research
  • Evidence Synthesis Methodologies

Background:

  • Generative artificial intelligence (GenAI) tools are increasingly accessible for research.
  • GenAI is proposed as a tool to accelerate evidence synthesis.
  • This study evaluates GenAI's performance in evidence synthesis compared to traditional methods.

Purpose of the Study:

  • To assess the accuracy, error rates, and time-saving potential of GenAI in evidence synthesis.
  • To compare GenAI's performance against expert-driven evidence synthesis approaches.

Main Methods:

  • A systematic review was conducted searching five databases on January 17, 2025.
  • Outcomes included accuracy, error rates, and time taken for GenAI in evidence synthesis.
  • Risk-of-bias was appraised using a modified QUADAS-2 tool.

Main Results:

  • GenAI missed 68%-96% of studies during searching.
  • Incorrect screening decisions ranged from 0%-29% for inclusion and 1%-83% for exclusion.
  • Data extraction errors (4%-31%) and risk-of-bias assessment errors (10%-56%) were also observed.

Conclusions:

  • Current evidence does not support unsupervised GenAI use in evidence synthesis.
  • GenAI may assist humans in evidence synthesis for tasks other than initial searching.
  • Human oversight is crucial for reliable GenAI-assisted evidence synthesis.