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.

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