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Assessing the Quality of Biomedical Boolean Search Strings Generated by Prompted and Unprompted Models Using ChatGPT:

Robyn B Reed¹, Derek J Barnett²

¹Harrell Health Sciences Library, Penn State College of Medicine, Biomedical Informatics and Emerging Technologies Librarian, 500 University Dr, Hershey, PA, USA.

Medical Reference Services Quarterly

|December 18, 2024

Summary

Generative AI, like ChatGPT, can create PubMed search strings, but results showed moderate overlap, not full replication, compared to published systematic reviews. Prompted and unprompted AI approaches showed minimal differences.

Keywords:

Artificial intelligence Boolean searching ChatGPT GenAI generative AI systematic reviews

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Area of Science:

Bibliometrics
Artificial Intelligence in Research
Information Science

Background:

Systematic reviews rely on comprehensive literature searches.
PubMed is a primary database for biomedical literature.
Boolean search strings are crucial for effective database querying.

Purpose of the Study:

To evaluate Generative AI (ChatGPT) for creating PubMed Boolean search strings.
To compare the effectiveness of AI-generated search strings against published ones.
To determine the optimal approach for using ChatGPT in search string formation.

Main Methods:

A pilot study compared search results from ChatGPT-generated Boolean strings to those from published systematic reviews.
Two methods were tested: prompted and unprompted ChatGPT queries.
Overlap in retrieved publications was quantified.

Main Results:

Moderate overlap was observed between publications retrieved by AI-generated and published search strings.
AI-generated strings did not fully replicate the outputs of published strings.
There was little discernible difference in effectiveness between prompted and unprompted ChatGPT usage.

Conclusions:

Generative AI shows potential for assisting in Boolean search string creation for PubMed.
Current AI capabilities provide moderate, but not complete, replication of established search strategies.
Further research is needed to refine AI approaches for systematic review searching.