Artificial Intelligence-Powered Precision Medicine for Cardi-ovascular Disease Prevention and Management
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. 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
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This study adopts a systematic bibliometric analysis to explore the research landscape sur-rounding artificial intelligence, precision medicine, cardiovascular disease, and prevention. A structured approach was employed, integrating the PRISMA framework for screening and selection, along with tools such as VOSviewer, Harzing's Publish or Perish, and Microsoft Excel for data extraction, analysis, and visualization. The data for this study was sourced from the Scopus database, chosen for its extensive and high-quality coverage of peer-reviewed journals. The search strategy involved a carefully constructed keyword query encompassing terms like "artificial intelligence," "precision medicine," "cardiovascular disease," and "prevention." The search parameters were restrict-ed to journal articles published in English between 2020 and 2024. This initial search yield-ed 426 records, which were subjected to further screening. The PRISMA framework was employed to ensure a transparent and systematic screening process. Duplicate records were removed, and articles that fell outside the scope of the study were excluded. The inclusion criteria focused on publications that addressed the intersection of artificial intelligence and healthcare, particularly those exploring precision medicine and cardiovascular disease. Following the screening process, 137 records were identified as rele-vant and included for bibliometric analysis. The analysis relied on several specialized tools to extract meaningful insights from the data. VOSviewer was used to visualize and analyze bibliometric networks, including keyword co-occurrence maps and collaborative relationships between countries. These visualizations helped to identify key research themes, clusters, and partnerships. Harzing's Publish or Per-ish was utilized to calculate critical citation metrics such as the h-index, g-index, and cita-tions per paper, providing a quantitative assessment of the research impact. Microsoft Excel was employed for data preprocessing, including filtering, categorization, and performing quantitative analyses to uncover trends in publication activity, journal contributions, and au-thor productivity. The results of this analysis offered valuable insights into the research dynamics of artificial intelligence-driven healthcare advancements. Key findings included trends in publication and citation activity, contributions of leading authors and institutions, and thematic clusters within the field. The combination of bibliometric tools and a systematic approach ensured the findings were robust, reproducible, and reflective of the current state of research. This methodology provides a comprehensive overview of the topic, offering a foundation for identifying gaps and opportunities for future research directions.
Institutions
- Cyberjaya University College of Medical Sciences