Evidence map›Paper›PMID 42403495›Full record

ArticleJournal of the Association of Medical Microbiology and Infectious Disease Canada = Journal officiel de l'Association pour la microbiologie medicale et l'infectiologie Canada2026

Integrating AI into infection control: Evaluating the accuracy and consistency of four leading platforms across three regions.

Julia Julkipli, Alhanouf Alanazi, Yen Tsen Saw, Ummu Afeera Zainulabid, Johan Delport, Ruchika Gupta, Michael Silverman, MohammadReza Rahimi Shahmirzadi, Sameer Elsayed, Fatimah AlMutawa

Abstract read
In one paragraph

Article in Journal of the Association of Medical Microbiology and Infectious Disease Canada = Journal officiel de l'Association pour la microbiologie medicale et l'infectiologie Canada, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Julia JulkipliDivision of Infectious Diseases, Western University, London, Ontario, Canada.
Alhanouf AlanaziDivision of Infectious Diseases, Western University, London, Ontario, Canada.
Yen Tsen SawDepartment of Infectious Diseases, North Manchester General Hospital, United Kingdom.
Ummu Afeera ZainulabidDepartment of Internal Medicine, Kulliyyah of Medicine, International Islamic University, Malaysia.
Johan DelportDepartment of Pathology and Laboratory Medicine, Western University, London, Ontario, Canada.
Ruchika GuptaDepartment of Pathology and Laboratory Medicine, Western University, London, Ontario, Canada.
Michael SilvermanDivision of Infectious Diseases, Western University, London, Ontario, Canada.ORCID https://orcid.org/0000-0002-0389-7656
MohammadReza Rahimi ShahmirzadiDivision of Infectious Diseases, Western University, London, Ontario, Canada.ORCID https://orcid.org/0000-0002-2410-6788
Sameer ElsayedDivision of Infectious Diseases, Western University, London, Ontario, Canada.
Fatimah AlMutawaDepartment of Internal Medicine, Kulliyyah of Medicine, International Islamic University, Malaysia.ORCID https://orcid.org/0000-0002-3978-1823

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Artificial Intelligence (AI) has emerged as a valuable tool in health care, supporting diagnostics and decision making. However, integration into clinical practice presents challenges, including data quality, accessibility, and regional guideline variations. This study evaluates four major AI platforms (ChatGPT, Meta AI, Copilot, and OpenEvidence) against CDC infection control guidelines for varicella and measles across Canada, Malaysia, and the United Kingdom.The objective was to assess the accuracy and consistency of AI responses on infection control measures compared with CDC guidelines and to evaluate how platforms handle complex scenarios. Methods: A comparative analysis of the four AI platforms was conducted using structured questions and clinical case scenarios on varicella and measles. Responses were evaluated for alignment with CDC guidelines. Platform accessibility was tested from Canada, Malaysia, and the United Kingdom, and regional variations were analyzed. Results: All platforms provided generally accurate information, but discrepancies were noted. ChatGPT and Meta AI mostly aligned with CDC guidelines, while OpenEvidence and Copilot omitted key epidemiological criteria. Meta AI lacked a full explanation of varicella laboratory criteria and was inaccessible in Malaysia. Regional differences in measles postexposure prophylaxis (PEP) were observed, particularly in Copilot and OpenEvidence. Response consistency varied between platforms. Conclusions: AI platforms show promise in supporting infection control but exhibit regional variability. Continued refinement of AI tools is essential to ensure their global applicability and accuracy. Consultation with an infection prevention and control (IPAC) physician remains vital for complex cases.

Indexed as

artificial intelligence (AI)case definitioninfection control measuresinfectious periodMalaysiameaslesvaricella

Identifiers

PMID42403495
PMCPMC13331609

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.