Evidence map›Paper›PMID 42740749›Full record

ArticleClinical and public health guidelines2026

GIN-McMaster Guideline Development Checklist Extension for Integrating Artificial Intelligence in the Health Guideline Enterprise.

Manuel Marques-Cruz, Bernardo Sousa-Pinto, Ignacio Neumann, Yuan Chi, Monika Nothacker, Artur Nowak, Marge Reinap, Mariette Awad, Pablo Alonso-Coello, Amir Qaseem and 5 more

Abstract read
In one paragraph

Article in Clinical and public health guidelines, 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

15 authors.

Manuel Marques-CruzMEDCIDS - Department of Community Medicine, Information and Health Decision Sciences; Faculty of Medicine University of Porto Porto Portugal.
Bernardo Sousa-PintoMEDCIDS - Department of Community Medicine, Information and Health Decision Sciences; Faculty of Medicine University of Porto Porto Portugal.ORCID https://orcid.org/0000-0002-1277-3401
Ignacio NeumannSchool of Medicine Universidad San Sebastián Santiago Chile.
Yuan ChiClinical Epidemiology and Research Center (CERC) Humanitas University Milan Italy.
Monika NothackerAWMF (Association of the Scientific Medical Societies in Germany) Institute for Medical Knowledge Management, c/o University of Marburg, Marburg Germany.
Artur NowakEvidence Prime Hamilton Ontario Canada.ORCID https://orcid.org/0000-0001-7862-5797
Marge ReinapWHO Regional Office for Europe Copenhagen Denmark.
Mariette AwadDepartment of Electrical and Computer Engineering American University of Beirut Beirut Lebanon.
Pablo Alonso-CoelloInstitut de Recerca Sant Pau (IR Sant Pau), and CIBER Epidemiología y Salud Pública (CIBERESP) Barcelona Spain.
Amir QaseemAmerican College of Physicians Pennsylvania Philadelphia USA.
Elie A AklDepartment of Internal Medicine American University of Beirut Beirut Lebanon.
Wojtek WierciochDepartment of Health Research Methods, Evidence and Impact, and the Department of Medicine McMaster University Hamilton Ontario Canada.ORCID https://orcid.org/0000-0001-6576-1650
Jan BrozekEvidence Prime Hamilton Ontario Canada.
Holger J SchünemannClinical Epidemiology and Research Center (CERC) Humanitas University Milan Italy.ORCID https://orcid.org/0000-0003-3211-8479
GIN AI working group

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Artificial intelligence (AI) may support several processes of the health guideline enterprise. This article describes the development of an extension of the Guidelines International Journal (GIN)-McMaster Guideline Development Checklist (GDC) for integrating AI in the guideline enterprise. This development has been led by the GIN-AI Working Group. Methods: We started by prompting a large language model (LLM) for items related to the use of AI in each of the steps of the original GDC. Subsequently, the members of the working group engaged in a set of iterative discussions, resulting in item refinement and in a consensus first version of the extension. We retrospectively applied this first version to a case use of guidelines incorporating AI in their development (Allergic Rhinitis and its Impact on Asthma [ARIA] 2024-2025 guidelines), leading to further refinement and to the approval of the final extension tool. Results: Prompting LLMs resulted in the generation of 149 items. Of those, 117 were removed and 19 were modified by members of the working group. On the other hand, 17 new items were added during the iterative discussion process. The retrospective application of the extension led to changes in the wording of four items. The final version of the checklist extension has been approved with 49 items modifying or adding to the original GDC. Discussion: We have developed an extension of the GIN-McMaster GDC that encompasses a set of conduct standards that are intended to facilitate the comprehensive and transparent integration of AI in the health guideline enterprise. Clinical Trial Registration: Not applicable. This study is not a clinical trial.

Identifiers

PMID42740749
PMCPMC13563358

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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.