Harnessing artificial intelligence for scalable evidence synthesis in reviews: Application in a bibliometric analysis of physical activity technologies.

George Thomas, Stephanie Alley, Meighan Browne, Hannes Baumann, Mitch J Duncan, Corneel Vandelanotte, Nicholas D Gilson

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Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

George ThomasHealth and Wellbeing Centre for Research Innovation, School of Human Movement and Nutrition Sciences, The University of Queensland, Brisbane, Australia.ORCID https://orcid.org/0000-0001-6908-7974

Stephanie AlleyAppleton Institute, School of Health, Medical and Applied Sciences, Central Queensland University, Wayville, South Australia, Australia.

Meighan BrowneSchool of Medicine & Public Health, The University of Newcastle, Callaghan, Australia.

Hannes BaumannInstitute for Movement Therapy and Movement-oriented Prevention and Rehabilitation, German Sport University Cologne, Cologne, Germany.

Mitch J DuncanSchool of Medicine & Public Health, The University of Newcastle, Callaghan, Australia.

Corneel VandelanotteAppleton Institute, School of Health, Medical and Applied Sciences, Central Queensland University, Wayville, South Australia, Australia.ORCID https://orcid.org/0000-0002-4445-8094

Nicholas D GilsonSchool of Human Movement and Nutrition Sciences, The University of Queensland, Brisbane, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Artificial intelligence (AI) tools offer promising opportunities to support evidence synthesis at scale. This study presents a novel AI-human hybrid screening approach to a large-scale bibliometric analysis of technologies promoting physical activity. Methods: Records ( Results: In Phase 1, a random 1% sample ( Conclusion: AI-assisted screening offers a feasible and efficient approach for large-scale evidence synthesis when supported by structured workflows and safeguards. While methods like careful seed selection and stopping rules improve rigour, challenges remain-particularly residual risks and reliance on manual data extraction. Future work should focus on extending AI to downstream tasks and embedding human-in-the-loop approaches to ensure it serves as a reliable, transparent partner in evidence synthesis.

Indexed as

artificial intelligencebibliometric analysisevidence synthesismachine learningphysical activity

Identifiers

PMID42311847

DOI10.1177/20552076261455158

PMCPMC13269980

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LicenceCC BY-NC

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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.