Big data, artificial intelligence, and cardiovascular precision medicine

ABSTRACT

Introduction: Cardiovascular diseases (CVDs) are chronic, heterogeneous diseases which are generally classified according to clinical presentation. However, the arrival of big data and analytical methods presents an opportunity to better understand these disease entities.

Areas covered: This review article highlights: (1) the potential of a big data approaches with emerging technology to explore the heterogeneity of CVDs; (2) current challenges of a big data approach; and (3) the future of precision cardiovascular medicine.

Expert commentary: Overall, most of the current data utilizing big data techniques remain largely descriptive and retrospective. Precision medicine, or N-of-1, approaches have not yet allowed for consistent interpretation since there is no ‘standard’ of how to best apply treatment approaches in a field where evidence-based medicine is based largely on randomized controlled trials. The risk score and biomarker-based approaches have been utilized with some ‘validation’ studies, but more in-depth biomarkers (i.e. pharmacogenomic biomarkers) have failed to demonstrate incremental benefits. Exploring novel CVD phenotypes by integrating existing medical variables, multi-omics, lifestyle, and environmental data using artificial intelligence is vitally important and may allow us to digitize future clinical trials, potentially leading to novel therapies.

KEYWORDS:

• Big data
• cardiovascular precision medicine
• precision medicine
• big data approach
• omics

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

This paper was not funded.