Artificial Intelligence in Cardiology: Applications, Benefits and Challenges

The introduction of such digital technologies as robotic implants, home monitoring devices, wearable sensors and mobile apps in healthcare have produced significant amounts of data, which need to be interpreted and operationalised by physicians and healthcare systems across disparate fields.1 Most often, such technologies are implemented at the patient level, with patients becoming their own producers and consumers of personal data, something which leads to them demanding more personalised care.2

This digital transformation has led to a move away from a ‘top-down’ data management strategy, “which entailed either manual entry of data with its inherent limitations of accuracy and completeness, followed by data analysis with relatively basic statistical tools… and often without definitive answers to the clinical questions posited”.3 We are now in an era of a ‘bottom-up’ data management strategy that involves real-time data extraction from various sources (including apps, wearables, hospital systems, etc.), transformation of that data into a uniform format, and loading of the data into an analytical system for final analysis.3