Evidence map›Paper›PMID 39242425›Full record

ReviewThe Egyptian heart journal : (EHJ) : official bulletin of the Egyptian Society of Cardiology2024

Artificial intelligence: transforming cardiovascular healthcare in Africa.

Patrick Ashinze, Eniola Akande, Chukwu Bethrand, Eniola Obafemi, Olafisoye-Oragbade Oluwatosin David, Suleiman Nasiru Akobe, Ndubuisi Onyinyechukwu Joyce, Obidiegwu Jonathan Izuchukwu, Ngozi Peace Okoro

Abstract readReview
In one paragraph

Review in The Egyptian heart journal : (EHJ) : official bulletin of the Egyptian Society of Cardiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Review
  2. Advancing Interventional Cardiology in Sub-Saharan Africa: The Role of Artificial Intelligence.Journal of the Society for Cardiovascular Angiography & Interventions · 2026
    Article
  3. Bridging the Workforce Gap in Ethiopian Catheterization Laboratories.Journal of the Society for Cardiovascular Angiography & Interventions · 2026
    Article
  4. Artificial intelligence in clinical toxicology in Africa: Emerging applications and barriers.African journal of emergency medicine : Revue africaine de la medecine d'urgence · 2025
    Review
  5. Review
  6. Review
  7. Article
  8. 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

9 authors.

Patrick AshinzeFaculty of Clinical Sciences, University of Ilorin, Ilorin, Nigeria. patrickashinze@yahoo.com.ORCID http://orcid.org/0009-0007-9137-5498
Eniola AkandeFaculty of Clinical Sciences, University of Ilorin, Ilorin, Nigeria.
Chukwu BethrandFaculty of Clinical Sciences, University of Ilorin, Ilorin, Nigeria.
Eniola ObafemiFaculty of Clinical Sciences, University of Ilorin, Ilorin, Nigeria.
Olafisoye-Oragbade Oluwatosin DavidFaculty of Clinical Sciences, University of Ilorin, Ilorin, Nigeria.
Suleiman Nasiru AkobeFaculty of Clinical Sciences, University of Ilorin, Ilorin, Nigeria.
Ndubuisi Onyinyechukwu JoyceChukwuemeka Odumegwu Ojukwu University Teaching Hospital, Anambra, Nigeria.
Obidiegwu Jonathan IzuchukwuFaculty of Clinical Sciences, University of Nigeria, Nsukka, Nigeria.
Ngozi Peace OkoroDepartment of Medicine, David Umahi Federal University Teaching Hospital, Uburu, Ebonyi State, Nigeria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCardiovascular diseases (CVDs), a significant global health concern, are responsible for 13% of all deaths particularly in Africa, where they contribute substantially to the global disease burden, taking several millions of lives globally and annually. Despite advancements in healthcare, the burden of CVDs continues to rise steadily. This comprehensive review critically examines the intersection of artificial intelligence (AI) and cardiovascular disease (CVD) management in Africa. Drawing on a diverse gamut of scholarly literature and empirical evidence, the review assesses the prevalence, impact, and challenges of CVDs in the African context. MAIN BODY: The review highlights the potential of AI technologies to revolutionize CVD care, offering insights into its applications in diagnosis, treatment optimization, and remote patient monitoring. It explores existing literature sourced from databases like PUBMED, Scopus and Google Scholar about the current state of AI implementation in African healthcare systems, which are majorly resource-constrained, discussing successes, limitations, and future prospects. The work includes the prevalence and impact of CVDs in Africa, noting the significant public health burden and economic implications. Current challenges in addressing CVDs are outlined, focusing on resource constraints, healthcare system challenges, and socioeconomic factors. Our review takes a dive into AI's role in healthcare, emphasizing its capabilities in disease diagnosis, treatment optimization, and patient monitoring, and presents current applications and case studies of AI in African cardiovascular healthcare. It also addresses the challenges and limitations of implementing AI in this context, such as inadequate infrastructure, lack of high-quality data, and the need for regulatory frameworks.

conclusionOur review emphasizes the urgent need for collaborative efforts among policymakers, healthcare providers, and researchers to overcome barriers to AI integration and ensure equitable access to innovative healthcare solutions. By fetching existing research and offering practical recommendations, this review contributes to the academic discourse on AI-driven healthcare interventions in Africa, offering an understanding of the opportunities and challenges in leveraging technology to address pressing public health concerns. It calls for increased research, investment, and collaboration to harness AI's full potential in transforming cardiovascular healthcare in Africa.

Indexed as

AfricaArtificial IntelligenceCardiovascular diseases

Identifiers

PMID39242425
PMCPMC11379677

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.