ArticleJournal of the Medical Library Association : JMLA2026
Evaluating automated or artificial intelligence search tools for evidence synthesis.
Article in Journal of the Medical Library Association : JMLA, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
1 author.
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Abstract
To advance information retrieval science for producing evidence syntheses at Canada's Drug Agency, the Research Information Services team developed a replicable process to evaluate automated or artificial intelligence (AI) search tools. The team inventoried 51 tools in the fall of 2023 and built a flexible evaluation instrument to inform adoption decisions and enable comparison between tools. Building on this foundational evaluation work, the team further conducted a comparative analysis on three top-ranked tools in the fall of 2024. The investigation confirmed that these automated or AI tools have inconsistent and variable performance for the range of information retrieval tasks performed by Information Specialists at Canada's Drug Agency. Implementation recommendations from this study informed a "fit for purpose" approach where Information Specialists leverage automated or AI search tools for specific tasks or project types.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.