Evidence map›Paper›PMID 42662848›Full record

ArticleEuropean heart journal. Digital health2026

Comparative evaluation of artificial intelligence-assisted literature search tools for identifying clinically meaningful evidence in cardiology.

Riccardo Di Febo, Maximiliano Jeanneret Medina, Alexandre Renaud, Maryam Dridi, Valentine Pecriaux, Benoit Lequeux, Stephane Lafitte, Baptiste Maille, Aymeric Menet

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in European heart journal. Digital health, 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

9 authors.

Riccardo Di FeboDepartment of Cardiology, Hôpital Saint Philibert, Groupement des Hôpitaux de L'Institut Catholique de Lille, Université Catholique de Lille, 115 Rue du Grand But, 59160 Lille, France.ORCID https://orcid.org/0009-0004-9378-5026
Maximiliano Jeanneret MedinaHEG Arc, HES-SO, University of Applied Sciences Western Switzerland, 2000 Neuchâtel, Switzerland.
Alexandre RenaudEM Normandie Business School, Métis Lab, 30-32, Rue Henri-Barbusse, Clichy 92110, France.ORCID https://orcid.org/0000-0001-7367-6640
Maryam DridiDepartment of Cardiology, Hôpital Saint Philibert, Groupement des Hôpitaux de L'Institut Catholique de Lille, Université Catholique de Lille, 115 Rue du Grand But, 59160 Lille, France.ORCID https://orcid.org/0009-0000-5905-2591
Valentine PecriauxDepartment of Cardiology, Hôpital Saint Philibert, Groupement des Hôpitaux de L'Institut Catholique de Lille, Université Catholique de Lille, 115 Rue du Grand But, 59160 Lille, France.
Benoit LequeuxCardiology Department, University Hospital of Poitiers, 2 Rue de la Milétrie, Poitiers F-86021, France.
Stephane LafitteBordeaux University, Department of Cardiology, Bordeaux University Hospital, Bordeaux F-33000, France.
Baptiste MailleDepartment of Cardiology, Marseille University Hospital Timone, 13005 Marseille, France.ORCID https://orcid.org/0000-0003-2639-9593
Aymeric MenetDepartment of Cardiology, Hôpital Saint Philibert, Groupement des Hôpitaux de L'Institut Catholique de Lille, Université Catholique de Lille, 115 Rue du Grand But, 59160 Lille, France.ORCID https://orcid.org/0000-0003-4823-1556

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aims: The rapid expansion of biomedical literature challenges clinicians' and researchers' ability to identify clinically meaningful evidence. We systematically compared five literature search tools, four artificial intelligence (AI)-assisted and one conventional, across clinically relevant cardiology research scenarios, using a blinded expert-validated gold standard to assess their ability to retrieve relevant and key references. Methods and results: We evaluated ChatGPT-5, Elicit, Consensus, Scite, and PubMed across four cardiology topics defined by maturity and specificity, with multiple standardized prompts. Three electrophysiology experts independently and blindly rated all retrieved references, defining two gold standards: expert-rated relevance and expert-selected key references. ChatGPT-5 achieved the highest proportion of relevant articles (90% [88-100], Conclusion: AI-assisted tools showed heterogeneous performance, ChatGPT-5 performing best in this cardiology setting. These preliminary, context-specific findings support hybrid human-AI strategies in which AI complements rather than replaces transparent database searches such as PubMed; larger-scale, multi-domain studies are needed to confirm and generalize them.

Indexed as

Artificial intelligenceCardiac resynchronization therapyDigital healthLiterature search

Identifiers

PMID42662848
PMCPMC13520940

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.