Evidence map›Paper›PMID 41246140›Full record

ArticleF1000Research2024

Assessing Africa's position in the development of AI-enabled ECG devices.

Hamza Ameziane, Yassine Zahidi, Mohamed El-Moufid, Hicham Medromi, Nadia Machkour, Nabila Rabbah

Abstract read
In one paragraph

Article in F1000Research, 2024. 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

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

1 citing paper in PubMed.

  1. Review
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

6 authors.

Hamza AmezianeLaboratory of Complex Cyber Physical Systems (LCCPS), Hassan II University of Casablanca National School of Arts and Crafts, Casablanca, Grand Casablanca, Morocco.ORCID https://orcid.org/0009-0008-0709-3031
Yassine ZahidiFoundation for Research Development and Innovation in Science and Engineering, Mohammedia, Morocco.
Mohamed El-MoufidFoundation for Research Development and Innovation in Science and Engineering, Mohammedia, Morocco.
Hicham MedromiFoundation for Research Development and Innovation in Science and Engineering, Mohammedia, Morocco.
Nadia MachkourLaboratory of Complex Cyber Physical Systems (LCCPS), Hassan II University of Casablanca National School of Arts and Crafts, Casablanca, Grand Casablanca, Morocco.
Nabila RabbahLaboratory of Complex Cyber Physical Systems (LCCPS), Hassan II University of Casablanca National School of Arts and Crafts, Casablanca, Grand Casablanca, Morocco.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The integration of Artificial Intelligence (AI) in electrocardiographic (ECG) devices has become a pivotal area of research, particularly during the COVID-19 pandemic. These technologies are essential for enhancing cardiac diagnosis and monitoring. Methods: This study assesses current trends, key contributors, and collaborative networks in the field of AI-enhanced ECG devices. We utilized a comprehensive analysis, using the Biblioshiny library from Bibliometrix for data exploration of data extracted from the Scopus database and VOSViewer for creating and visualizing maps. These tools were played an important role in conducting an in-depth analysis of the relationships and developments within the field. Results: The analysis shows a significant increase in publications related to AI-enhanced ECG devices, with a marked surge during the COVID-19 pandemic. Despite the growing interest and technological advancements, the study exposes a notable disparity in the geographical distribution of research contributions, highlighting substantial under-representation of African researchers. This gap is attributed to infrastructural, financial constraints, and limited collaborative networks within the continent. Conclusion: The rapid evolution and increasing importance of AI in ECG devices underscore the need for more inclusive research practices. There is a critical need to integrate and promote contributions from under-represented regions, particularly Africa, to ensure a globally diverse perspective in tackling health challenges. This study calls for enhanced participation and support for African researchers to bridge the existing research gap and foster global health equity.

Indexed as

Artificial IntelligenceCOVID-19ElectrocardiographyAfricaHumansPandemicsSARS-CoV-2Artificial Intelligence (AI)Cardiac Medicinecardiovascular diseaseElectrocardiogram (ECG)Healthcare TechnologyVOSviewer

Identifiers

PMID41246140
PMCPMC12612617

What OpenQuestion holds

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LicenceCC BY
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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.