Evidence map›Paper›PMID 41781892›Full record

ArticleBMC medical research methodology2026

Searching smarter, not harder: leveraging AI to enhance literature searches for theory-driven reviews-A methodological case study.

R Hunter, A Booth, L Wood

Abstract read
In one paragraph

Article in BMC medical research methodology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

R HunterDepartment of Public Health and Sports Sciences, Medical School Building, University of Exeter, St Luke's Campus, Exeter, EX1 2LU, UK. rebecca.j.hunter@northumbria.ac.uk.ORCID 0000-0001-7837-7260
A BoothSchool of Health and Related Research (ScHARR), The University of Sheffield, Sheffield City Centre, 30 Regent St, Sheffield, S1 4DA, UK.ORCID 0000-0003-4808-3880
L WoodDepartment of Public Health and Sports Sciences, Medical School Building, University of Exeter, St Luke's Campus, Exeter, EX1 2LU, UK.ORCID 0000-0003-1039-1642

Funding

National Institute for Health and Care Research NIHR205671
6 · The paper itself

Abstract

backgroundIntegrating artificial intelligence (AI) into literature searching has the potential to enhance research synthesis by improving the identification of conceptually rich or otherwise difficult-to-locate evidence. Theoretical or conceptual literature reviews, including realist reviews, often involve resource-intensive searches because they aim to trace nuanced ideas, mechanisms, or conceptual relationships across multiple sources. This case study illustrates the use of AI-powered tools to support and streamline such literature searching, using a realist review as an example.

methodsWe applied AI tools-Scite and Undermind-in the context of a realist review to facilitate the identification of relevant studies. Seed papers and key informant papers guided the search, and a novel classification system (grandparent, parent, and child papers) was used to systematically organise studies for developing and refining theoretical constructs. Transparent screening procedures and decision-making frameworks were employed to ensure methodological rigour and reproducibility.

resultsThe integration of AI tools supported the retrieval of conceptually relevant literature and helped manage complex datasets. The classification system enabled structured organisation of studies, supporting iterative testing and refinement of theoretical constructs. The workflow demonstrated flexibility and adaptability, suggesting potential applicability beyond realist review.

conclusionsOur findings suggest that AI-powered tools can support literature searching, particularly in identifying conceptually relevant studies. However, these tools do not replace the critical interpretive work required by researchers. Human judgement remains essential to assess relevance, evaluate nuanced concepts, and make informed decisions throughout the search process, with AI serving as a valuable adjunct rather than a substitute.

Indexed as

Artificial IntelligenceInformation Storage and RetrievalReview Literature as TopicHumansReproducibility of ResultsArtificial intelligenceEvidence appraisalLiterature screeningLiterature searchesRealist reviewsReview methodology

Identifiers

PMID41781892
PMCPMC13067685

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.