*Result*: The landscape of artificial intelligence tools and platforms for evidence synthesis: a scoping review.
Title:
The landscape of artificial intelligence tools and platforms for evidence synthesis: a scoping review.
Authors:
Sousa MSA; Department of Medicine, Department of Morphology and Genetics, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil. sharmila.sousa@gmail.com., Peiris S; Science and Knowledge for Impact Unit, Evidence and Intelligence for Action in Health, Pan American Health Organization, Washington, DC, USA., Figueiró MF; Hcor - Associação Beneficente Síria, São Paulo, Brazil., Haby MM; Faculty of Biological and Health Sciences, University of Sonora, Hermosillo, Mexico., Baraldi AC; Evidence and Intelligence for Action in Health, BIREME, Pan American Health Organization, Brasilia, Brazil., Reveiz L; Science and Knowledge for Impact Unit, Evidence and Intelligence for Action in Health, Pan American Health Organization, Washington, DC, USA., Souza JP; Evidence and Intelligence for Action in Health, BIREME, Pan American Health Organization, Brasilia, Brazil.
Source:
Systematic reviews [Syst Rev] 2026 Feb 10; Vol. 15 (1). Date of Electronic Publication: 2026 Feb 10.
Publication Type:
Journal Article; Scoping Review
Language:
English
Journal Info:
Publisher: BioMed Central Country of Publication: England NLM ID: 101580575 Publication Model: Electronic Cited Medium: Internet ISSN: 2046-4053 (Electronic) Linking ISSN: 20464053 NLM ISO Abbreviation: Syst Rev Subsets: MEDLINE
Imprint Name(s):
Original Publication: London : BioMed Central
MeSH Terms:
References:
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Entry Date(s):
Date Created: 20260210 Date Completed: 20260318 Latest Revision: 20260320
Update Code:
20260320
PubMed Central ID:
PMC12998105
DOI:
10.1186/s13643-025-02842-y
PMID:
41664235
Database:
MEDLINE
*Further Information*
*Evidence synthesis (ES) involves rigorous, reproducible methodologies, which are increasingly being presented as 'Living' systematic reviews. As such, ES are critical to evidence-informed decision-making processes, such as the development, implementation, evaluation and monitoring of health technology assessments, practice guidelines and policies. However, the ES process is time-intensive, typically requiring months or years and extensive manual effort. Technological advancements, particularly artificial intelligence (AI), offer opportunities to automate various ES steps, potentially increasing efficiency and reducing costs. AI tools and platforms, including large language models (LLMs), facilitate faster ES through advanced natural language processing (NLP) capabilities. Despite their potential, AI tools have limitations, including risks of automation bias and lack of true semantic understanding, requiring careful evaluation to ensure trustworthiness. We conducted the first scoping review to update and map all data science tools, including LLMs, which are either being developed and/or deployed to optimise ES steps and assess their impact in both low- and middle-income countries (LMICs) and high-income countries (HICs). Our scoping review identified 137 studies and 388 of such AI tools and platforms to respond to the World Health Organization's call for safe and ethical AI in health, documenting the current landscape to identify barriers and facilitators to equitable and sustainable access for glocal researchers. We further outline three recommendations: (1) promote collaborative AI platforms ensuring equity of access to include gap regions identified (Latin America, Africa, Middle East), (2) establish evaluation standards for methods testing and reporting, and (3) emphasise human input and multidisciplinary capacity building for developing and implementing AI tools in ES.
(© 2026. Pan American Health Organization.)*
*Declarations. Ethics approval and consent to participate: Ethical approval is not required for scoping reviews. Consent for publication: Not applicable. Competing interests: The authors declare that they have no competing interests.*