Pharmacogenomics and Artificial Intelligence in Cardiovascular Disease: Emerging Tools for Precision Medicine

The article proposes combining pharmacogenomics and artificial intelligence as a new paradigm for precision-medicine management of cardiovascular disease. Relative to conventional therapy that inadequately acknowledges individual genetic and clinical variation, the article integrates genetic explanations of drug response with data-driven diagnosis, risk assessment, and treatment decisions in one framework. This is a literature-based review; searches covered PubMed, Google Scholar, Scopus, and Web of Science for literature published from 1990 to 2026, with emphasis on recent and clinically relevant studies, and no specific study counts or quantitative results are given.

Pharmacogenomics is described as elucidating the genetic basis of individual drug responses, thereby improving treatment success and minimizing adverse drug effects. The article attributes individual differences in drug response to genetic basis rather than relying only on conventional clinical judgment. A general statement within the review; the text provides no specific genes, drugs, or effect sizes.

Artificial intelligence is described as improving diagnostic accuracy, risk assessment, and treatment strategy formulation by analyzing extensive and complex clinical, genetic, and imaging datasets. The article emphasizes that AI handles multi-source, large-scale, complex data rather than a single data type. Also a review-level generalization; the text lists no specific algorithms, dataset sizes, or performance metrics.

The article identifies data security, ethical concerns, and insufficient clinical validation as obstacles hindering widespread adoption of these technologies. While affirming the application prospects, the article explicitly lists these three issues as conditions for adoption. A review-level judgment; the text does not elaborate specific cases or quantitative evidence.

The article is intended for readers interested in precision medicine for cardiovascular disease and is suited to understanding the overall framework and potential application scenarios of combining pharmacogenomics with artificial intelligence; its conclusions rest on literature searches from 1990 to 2026, with emphasis on recent and clinically relevant studies.

Readers may still watch for which specific gene-drug combinations relate to cardiovascular treatment, in which datasets and clinical settings AI models have been validated, and how data-security and ethical frameworks would be implemented; the text does not elaborate these details, so further consultation of primary studies is advisable before making clinical or technical decisions.