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Leveraging artificial intelligence for evidence synthesis

Leveraging AI for Evidence Synthesis Teaser

Abstract

Well-conducted evidence syntheses are the cornerstone of evidence-based health care decisions.1 Whether systematic reviews, scoping reviews, or qualitative evidence syntheses, these approaches are designed to provide decision-makers with the best available evidence through systematic, rigorous, and transparent methods.2 Over recent decades, the scientific community has advanced methods for every step of the evidence synthesis process to increase trustworthiness and rigor. Yet these gains have also increased the workload: evidence syntheses are resource-intensive and can take a year or longer to complete.3 For those making health care decisions – in clinical practice or at organizational and policy levels – this is often too long.


Citation

Barbara Nussbaumer-Streit, Marc Streit, Kylie Porritt
Leveraging artificial intelligence for evidence synthesis
JBI Evidence Synthesis, 24(7): 1280-1282, doi:10.11124/JBIES-26-00371, 2026.

BibTeX

@article{2026_leveraging_ai,
    title = {Leveraging artificial intelligence for evidence synthesis},
    author = {Barbara Nussbaumer-Streit and Marc Streit and Kylie Porritt},
    journal = {JBI Evidence Synthesis},
    publisher = {Ovid Technologies (Wolters Kluwer Health)},
    doi = {10.11124/JBIES-26-00371},
    volume = {24},
    number = {7},
    pages = {1280-1282},
    month = {7},
    year = {2026}
}