Evidence map›Paper›PMID 41099922›Full record

ArticleThe Egyptian heart journal : (EHJ) : official bulletin of the Egyptian Society of Cardiology2025

Performance of an artificial intelligence-powered smartphone application in the UK clinical settings: ECG automation compared to healthcare professionals.

Ahmed Kassem, John Folkes, Sahil Mukherjee, James Rosengarten

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Article in The Egyptian heart journal : (EHJ) : official bulletin of the Egyptian Society of Cardiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. 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

4 authors.

Ahmed KassemCardiology Department, East Kent Hospitals University NHS Foundation Trust, Ashford, United Kingdom. ahmed.kassem@nhs.net.
John FolkesCardiology Department, East Kent Hospitals University NHS Foundation Trust, Ashford, United Kingdom.
Sahil MukherjeeKing's College London, London, United Kingdom.
James RosengartenCardiology Department, East Kent Hospitals University NHS Foundation Trust, Ashford, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe electrocardiogram (ECG) is widely used in clinical practice, but accurate interpretation requires significant expertise. Variability in training leads to inconsistent diagnostic accuracy amongst healthcare professionals. Artificial intelligence (AI) applications, such as PMCardio (Powerful Medical, Samorin, Slovakia), can digitise and interpret ECGs. While validated in selected populations, its performance compared to clinicians in UK practice has not been assessed.

methodsSeventy-six healthcare professionals interpreted eight ECG traces (seven abnormal, one normal). Their performance was compared with the PMCardio application. Accuracy and time were recorded.

resultsHealthcare professionals achieved a mean accuracy rate of 67.1% (SD 24.0%), improving with seniority (junior 60%, mid-level 67.5%, senior 80%). PMCardio achieved perfect accuracy on the tested ECGs. Clinicians interpreted faster (median 23.7 s, range 9.1 s) compared to PMCardio (39.0 s, range 8.0 s), noting that the application's timing included operational steps such as loading and capturing ECG images.

conclusionsPMCardio demonstrated higher diagnostic accuracy than healthcare professionals but required longer interpretation times. Given the small dataset (8 ECGs) and lack of patient context, results should be interpreted cautiously. While AI tools may support clinicians and enhance consistency, they may also introduce uncertainty for less experienced users. Further studies with larger, real-world datasets are needed before widespread adoption.

Identifiers

PMID41099922
PMCPMC12532542

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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.