Evidence map›Paper›PMID 40676128›Full record

ArticleScientific reports2025

An academic evaluation of ChatGpt's ability and accuracy in creating patient education resources for rare cardiovascular diseases.

Samet Sevinç, Mustafa Candemir, Betül Ayça Yamak, Emrullah Kızıltunç, Burak Sezenöz, Orhan Batur Şahin, Salih Topal, Yusuf Demir, Mehmet Rıdvan Yalçın, Asife Şahinarslan

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

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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.

Samet SevinçDepartment of Cardiology, İstanbul Mehmet Akif Ersoy Thoracic and Cardiovascular Surgery Training and Research Hospital, 34303, İstanbul, Türkiye, Turkey.
Mustafa CandemirFaculty of Medicine, Department of Cardiology, Gazi University, Ankara, 06560, Türkiye, Turkey. mcandemir@gazi.edu.tr.
Betül Ayça YamakDepartment of Cardiology, Hopa State Hospital, Artvin, 08600, Türkiye, Turkey.
Emrullah KızıltunçFaculty of Medicine, Department of Cardiology, Gazi University, Ankara, 06560, Türkiye, Turkey.
Burak SezenözFaculty of Medicine, Department of Cardiology, Gazi University, Ankara, 06560, Türkiye, Turkey.
Orhan Batur ŞahinFaculty of Medicine, Department of Cardiology, Gazi University, Ankara, 06560, Türkiye, Turkey.
Salih TopalFaculty of Medicine, Department of Cardiology, Gazi University, Ankara, 06560, Türkiye, Turkey.
Yusuf DemirÇiğli Training and Research Hospital, Department of Cardiology, Bakırçay University, İzmir, 35620, Türkiye, Turkey.
Mehmet Rıdvan YalçınFaculty of Medicine, Department of Cardiology, Gazi University, Ankara, 06560, Türkiye, Turkey.
Asife ŞahinarslanFaculty of Medicine, Department of Cardiology, Gazi University, Ankara, 06560, Türkiye, Turkey.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Generative Pre-trained Transformer (ChatGPT) is a web-based artificial intelligence assistant with the potential to provide information, answer questions, and make recommendations on various topics. Rare cardiovascular diseases (rCVD) are among the health problems that require specialized knowledge and attention, and web databases provide relatively limited information. In this study, we investigated the accuracy and reliability of ChatGPT's answers to patients' possible questions about rCVD. ChatGPT was asked forty questions about rCVD. Based on current guidelines and information, academicians who are experts in their fields evaluated ChatGPT's answers to these questions. The success of ChatGPT, which has been repeatedly evaluated in classical diseases, was lower in rCVD. The responses to various questions exhibited significant similarity, with some answers including redundant information. In addition, ChatGPT did not give the desired answers to some questions. However, although some answers were longer than necessary, there was very little incorrect information in the answers. Although ChatGPT is competent in obtaining information about rCVD, physicians should clarify the answers given by ChatGPT to patients. Therefore, ChatGPT should be used as an auxiliary information acquisition tool rather than as a primary resource for patients with rCVD.

Indexed as

Artificial IntelligenceCardiovascular DiseasesPatient Education as TopicRare DiseasesGenerative Artificial IntelligenceHumansInternetReproducibility of ResultsAccuracyArtificial intelligenceChatGPTRare cardiovascular diseaseReliability

Identifiers

PMID40676128
PMCPMC12271526

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.