Evidence map›Paper›PMID 40984983›Full record

ArticleEuropean heart journal. Digital health2025

Large language models to develop evidence-based strategies for primary and secondary cardiovascular prevention.

Giulia Lorenzoni, Camilla Zanotto, Anna Sordo, Alberto Cipriani, Martina Perazzolo Marra, Francesco Tona, Daniele Gasparini, Dario Gregori

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In one paragraph

Article in European heart journal. Digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

8 authors.

Giulia LorenzoniUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padova, Italy.
Camilla ZanottoUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padova, Italy.
Anna SordoUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padova, Italy.
Alberto CiprianiCardiology Unit, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padova, Italy.
Martina Perazzolo MarraCardiology Unit, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padova, Italy.
Francesco TonaCardiology Unit, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padova, Italy.ORCID https://orcid.org/0000-0003-4828-7875
Daniele GaspariniUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padova, Italy.
Dario GregoriUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padova, Italy.ORCID https://orcid.org/0000-0001-7906-0580

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aims: Cardiovascular diseases are the leading global cause of mortality, with ischaemic heart disease contributing significantly to the burden. Primary and secondary prevention strategies are essential to reducing the incidence and recurrence of acute myocardial infarction. Healthcare professionals are no longer the sole source of health education; the Internet, including tools powered by artificial intelligence, is also widely utilized. This study evaluates the accuracy and the readability of large language model (LLM)-generated information on cardiovascular primary and secondary prevention. Methods and results: An observational study assessed LLM's responses to two tailored questions about acute myocardial infarction risk prevention. The LLM used was ChatGPT (4o version). Expert cardiologists evaluated the accuracy of each response using a Likert scale, while readability was assessed with the Flesch Reading Ease Score (FRES). ChatGPT-4o provided comprehensive and accurate responses for 15 out of 20 (75%) of the items. Readability scores were low, with median FRES indicating that both primary and secondary prevention content were difficult to understand. Specialized clinical topics exhibited lower accuracy and readability compared to the other topics. Conclusion: The current study demonstrated that ChatGPT-4o provided accurate information on primary and secondary prevention, although its readability was assessed as difficult. However, clinical oversight still remains critical to bridge gaps in accuracy and readability and ensure optimal patient outcomes.

Indexed as

Acute myocardial infarctionEducationLarge language modelsPrimary preventionSecondary prevention

Identifiers

PMID40984983
PMCPMC12450516

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Registered trials

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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.