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Summary
This summary is machine-generated.

Generative AI chatbots can create systematic review searches, identifying about 67-72% of studies. While useful for initial strategy development, they do not replace expert librarians.

Keywords:
database searchinggenerative artificial intelligencelarge language modelsystematic review

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Area of Science:

  • Information Science
  • Medical Librarianship
  • Artificial Intelligence in Research

Background:

  • Systematic reviews (SRs) demand extensive, reproducible searches, a process that is time-consuming and requires specialized expertise.
  • Generative AI (GAI) presents an opportunity to simplify SR search strategy development, but empirical evidence is limited.

Purpose of the Study:

  • To outline a method for creating a custom ChatGPT-based chatbot for SR search strategy development.
  • To assess the performance of this GAI-assisted approach.

Main Methods:

  • A cross-sectional evaluation utilized ChatGPT-4.0 to develop a chatbot mimicking a medical librarian for PICO-informed searches.
  • The chatbot was trained on methodological references and refined through pilot testing.
  • Queries were generated for 50 Cochrane SRs (2024) using P-I-O prompts for PubMed and Embase.

Main Results:

  • The chatbot achieved an 83.7% retrieval rate during pilot testing.
  • In the main evaluation, the chatbot identified a median of 67.4% of included studies (72.0% for indexed studies).
  • Performance decreased when outcomes were not in abstracts or interventions had varied terminology.

Conclusions:

  • GAI chatbots offer a rapid method for generating initial SR search strategies, achieving moderate study identification rates.
  • These tools serve as valuable starting points but cannot fully replace the expertise of information professionals.
  • Enhancing GAI performance may involve integrating librarian expertise, structured prompts, and controlled vocabularies, necessitating further research and transparent reporting.