Cardiovascular diseases (CVDs) are the leading global cause of early mortality and disability, characterized by their complex, multifactorial nature, which demands personalized diagnostic and therapeutic strategies. Current diagnostic methods for CVDs, while essential, have limitations in sensitivity, specificity, and real-time monitoring, which can impede early and accurate disease detection. Biomarkers emerge as pivotal tools for the enhanced diagnosis and management of CVDs, yet the challenges in their discovery and validation persist. Integrating artificial intelligence with omics technologies promises to revolutionize this field by significantly improving the precision and speed of biomarker discovery. This innovative approach, merging the analysis of genetic variants with expression patterns linked to specific phenotypes, holds the potential to uncover novel biomarkers and facilitate the stratification of patient groups based on individual risk factors, thereby advancing personalized medicine in cardiovascular care.
Acknowledgments
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Funding
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Conflict of interest
The authors declare that they have no competing interests.
Author contributions
Conceptualization: All authors
Writing - original draft: Christos Papaneophytou
Writing - review & editing: All authors
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