Evidence map›Paper›PMID 40853541›Full record

ArticleJournal of ultrasound2025

ChatGPT: the essential patient's companion for transesophageal echocardiogram education.

Yuval Avidan, Hussein Sliman, Baruch Weizman, Orel Ben Court, Alexander Fuks, Salim Adawi, Amir Aker

Abstract read
In one paragraph

Article in Journal of ultrasound, 2025. 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

7 authors.

Yuval AvidanDepartment of Cardiology, Lady Davis Carmel Medical Center, 7 Michal St., Haifa, Israel. Yuvalavidan10@gmail.com.ORCID http://orcid.org/0000-0002-8440-8892
Hussein SlimanDepartment of Cardiology, Lady Davis Carmel Medical Center, 7 Michal St., Haifa, Israel.
Baruch WeizmanDepartment of Internal Medicine, Lady Davis Carmel Medical Center, Haifa, Israel.
Orel Ben CourtDepartment of Internal Medicine, Lady Davis Carmel Medical Center, Haifa, Israel.
Alexander FuksDepartment of Cardiology, Lady Davis Carmel Medical Center, 7 Michal St., Haifa, Israel.
Salim AdawiDepartment of Cardiology, Lady Davis Carmel Medical Center, 7 Michal St., Haifa, Israel.
Amir AkerDepartment of Cardiology, Lady Davis Carmel Medical Center, 7 Michal St., Haifa, Israel.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTransesophageal echocardiography (TOE) necessitates meticulous patient preparation. Artificial intelligence (AI) chatbots, such as ChatGPT, hold promise in delivering medical information. This study sought to assess the suitability of the latest iteration, ChatGPT-4 Omni (GPT-4o), as a supplemental resource for providing information on topics related to TOE.

methodsFifty questions pertaining to patient education, covering both fundamental knowledge (25) and management aspects (25) of TOE results, were submitted to GPT-4o in three separate sessions. Three senior echocardiographers systematically evaluated the responses using a four-point scale, focusing on their appropriateness, clarity, and consistency. Additionally, they assessed confabulations and the omission of relevant content on a binary scale.

resultsOverall, 94% of GPT-4o's responses were rated as "highly appropriate" across 50 tasks, with 98% deemed "highly comprehensible". Responses related to basic knowledge were 96% appropriate and 100% comprehensible, while management-related responses were 92% appropriate and 96% comprehensible. In total, two responses (4%) were rated negatively as quite inappropriate. Minor inconsistencies were observed in 12% of regenerated responses, with relevant content missing in 6% of inquiries.

conclusionsGPT-4o demonstrated high accuracy and reliability in addressing patient inquiries related to TOE. These findings suggest that this AI platform could effectively supplement patient education and complement healthcare professionals' guidance, thereby reducing the workload of echocardiography lab staff.

Indexed as

Artificial IntelligenceEchocardiography, TransesophagealPatient Education as TopicGenerative Artificial IntelligenceHumansChatGPTPatient educationTransoesophageal echocardiography

Identifiers

PMID40853541
PMCPMC12675863

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.