Data · dataset · 2025
Artificial Intelligence-Powered Precision Medicine for Cardi-ovascular Disease Prevention and Management
Listed in Teesside University Research Data Repository
Description
Abstract
Artificial intelligence (AI) is transforming precision medicine, particularly in cardiovascular disease prevention and management. This bibliometric analysis examines the research land-scape from 2020 to 2024, focusing on AI's role in improving diagnostics, personalizing treatment, and advancing predictive healthcare. Using the PRISMA framework, VOSviewer, Harzing's Publish or Perish, and Excel, 137 articles from Scopus were systematically ana-lyzed.
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The study reveals a significant surge in research activity, with 2024 marking a peak. Machine learning and deep learning are central to key advancements, enabling early detec-tion and risk prediction. Contributions from leading institutions highlight the global and in-terdisciplinary nature of this field, with studies demonstrating AI's potential to integrate complex datasets and deliver tailored therapies.
While AI-driven innovations show promise, challenges such as ethical concerns and healthcare disparities remain. This analysis under-scores AI's transformative potential in precision medicine and identifies opportunities for equitable, collaborative advancements. Keywords: Artificial Intelligence, Machine Learning, Cardiovascular Disease, Heart Dis-ease, Coronary Artery Disease, Precision Medicine, Treatment
Links
Where it is published
- DOI doi.org/10.17632/hhv937v7pv.2 ↗
DOI / persistent id · from researchdata tees ac uk
Catalogue records · 1
- OAI-PMH record data.mendeley.com/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Adata… ↗
metadata API · from researchdata tees ac uk
Topics
Provenance · 1 source records, 13 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Teesside University Research Data Repository | oai:data.mendeley.com/hhv937v7pv.2 | 11 h ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[anatomy].local:anatomy:heart | enrichment · researchdata tees ac uk | keyword-concept-rules@1.0.0 | title+description (75%) |
| concepts[disease].local:disease:cardiovascular-disease | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Cardiovascular Disease'] |
| concepts[disease].local:disease:disease | enrichment · researchdata tees ac uk | keyword-concept-rules@1.0.0 | title+description (75%) |
| concepts[field].anzsrc:group:4602 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Artificial Intelligence'] |
| concepts[field].anzsrc:group:4611 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Machine Learning'] |
| concepts[field].local:field:computer-science-ai | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:medicine-health | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| description | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/description |
| license | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/rights |
| publication_date | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| title | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/title |