Evidence map›Paper›PMID 40933879›Full record

ArticleCochrane evidence synthesis and methods2025

Exploring the Role of Artificial Intelligence in Evidence Synthesis: Insights From the CORE Information Retrieval Forum 2025.

Claire H Eastaugh, Madeleine Still, Fiona R Beyer, Sheila A Wallace, Hannah O'Keefe

Abstract read
In one paragraph

Article in Cochrane evidence synthesis and methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
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

5 authors.

Claire H EastaughNIHR Innovation Observatory Newcastle University Newcastle-upon-Tyne UK.ORCID https://orcid.org/0000-0002-1371-6601
Madeleine StillNIHR Innovation Observatory Newcastle University Newcastle-upon-Tyne UK.ORCID https://orcid.org/0000-0003-0625-6325
Fiona R BeyerNIHR Innovation Observatory Newcastle University Newcastle-upon-Tyne UK.ORCID https://orcid.org/0000-0002-6396-3467
Sheila A WallaceNIHR Innovation Observatory Newcastle University Newcastle-upon-Tyne UK.ORCID https://orcid.org/0000-0003-2853-3653
Hannah O'KeefeNIHR Innovation Observatory Newcastle University Newcastle-upon-Tyne UK.ORCID https://orcid.org/0000-0002-0107-711X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Information retrieval is essential for evidence synthesis, but developing search strategies can be labor-intensive and time-consuming. Automating these processes would be of benefit and interest, though it is unclear if Information Specialists (IS) are willing to adopt artificial intelligence (AI) methodologies or how they currently use them. In January 2025, the NIHR Innovation Observatory and NIHR Methodology Incubator for Applied Health and Care Research co-sponsored the inaugural CORE Information Retrieval Forum, where attendees discussed AI's role in information retrieval. Methods: The CORE Information Retrieval Forum hosted a Knowledge Café. Participation was voluntary, and attendees could choose one of six event-themed discussion tables including AI. To support each discussion, a QR code linking to a virtual collaboration tool (Padlet; padlet.com) and a poster in the exhibition space were available throughout the day for attendee contributions. Results: The CORE Information Retrieval Forum was attended by 131 IS from nine different types of organizations, with most from the UK and ten countries represented overall. Among the six discussion points available in the Knowledge Café, the AI table was the most popular, receiving the highest number of contributions ( Conclusions: While there are critical perspectives on the integration of AI in the IS space, this is not due to a reluctance to adapt and adopt but from a need for structure, education, training, ethical guidance, and systems to support the responsible use and transparency of AI. There is interest in automating repetitive and time-consuming tasks, but attendees reported a lack of appropriate supporting tools. More work is required to identify the suitability of currently available tools and their potential to complement the work conducted by IS.

Indexed as

artificial intelligenceevidence synthesisgenerative AIinformation retrievalinformation specialistlarge language modelsliterature search

Identifiers

PMID40933879
PMCPMC12419556

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.