Hunter, R. orcid.org/0000-0001-7837-7260, Booth, A. orcid.org/0000-0003-4808-3880 and Wood, L. orcid.org/0000-0003-1039-1642 (2026) Searching smarter, not harder: leveraging AI to enhance literature searches for theory-driven reviews—A methodological case study. BMC Medical Research Methodology. ISSN: 1471-2288
Abstract
Background
Integrating 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.
Methods
We 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.
Results
The 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.
Conclusions
Our 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.
Metadata
| Item Type: | Article |
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| Authors/Creators: |
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| Copyright, Publisher and Additional Information: | © The Author(s) 2026. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| Keywords: | Artificial intelligence; Evidence appraisal; Literature screening; Literature searches; Realist reviews; Review methodology |
| Dates: |
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| Institution: | The University of Sheffield |
| Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Medicine and Population Health |
| Funding Information: | Funder Grant number National Institute for Health and Care Research NIHR205671 |
| Date Deposited: | 18 Mar 2026 14:27 |
| Last Modified: | 18 Mar 2026 14:27 |
| Status: | Published online |
| Publisher: | Springer Science and Business Media LLC |
| Refereed: | Yes |
| Identification Number: | 10.1186/s12874-026-02814-3 |
| Related URLs: | |
| Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:239229 |
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