The Year in Cardiovascular Medicine 2021: Digital Health and Innovation

Panos E. Vardas; Folkert W. Asselbergs; Maarten van Smeden; Paul Friedman


Eur Heart J. 2022;43(4):271-279. 

In This Article

Abstract and Introduction


Graphical Abstract: Digital tool development in cardiovascular medicine

This article presents some of the most important developments in the field of digital medicine that have appeared over the last 12 months and are related to cardiovascular medicine. The article consists of three main sections, as follows: (i) artificial intelligence-enabled cardiovascular diagnostic tools, techniques, and methodologies, (ii) big data and prognostic models for cardiovascular risk protection, and (iii) wearable devices in cardiovascular risk assessment, cardiovascular disease prevention, diagnosis, and management. To conclude the article, the authors present a brief further prospective on this new domain, highlighting existing gaps that are specifically related to artificial intelligence technologies, such as explainability, cost-effectiveness, and, of course, the importance of proper regulatory oversight for each clinical implementation.


Digital health, a broad-spectrum concept that has received a significant boost as a result of the COVID-19 pandemic, is growing exponentially, flexing its muscles with scientific breakthroughs and associated publications, while also driving trends and developments in industry.

For cardiovascular medicine in particular, during the last year, an impressive number of authoritative new publications have confirmed previous research findings and proposed new innovative ideas and practices related to the diagnostic and therapeutic management of cardiovascular diseases, with the promise of ground-breaking developments during the coming years, for both cardiovascular sciences and care.

In the year 2021, as in the years immediately preceding, the field of digital health has been flooded with publications referring to the diverse applications of artificial intelligence (AI), from supervised to unsupervised learning, focusing mainly on the diagnostic capabilities of this impressive new technology.

Furthermore, the role of machine learning algorithms in the development of clinical prognostic models for risk assessment and early warning systems represents a rapidly evolving field that may be expected to have a catalytic effect by improving the prediction of medium- and long-term clinical outcomes.

Indeed, the prospects seem to be excellent.

Nonetheless, some questions still remain. Apart from the in silico design and development, the explainability of the machine learning algorithms and their validation methodology need to be more solidly confirmed in well-designed longitudinal studies, as well as in clinical practice before these algorithms find their way into the guidelines.

Beyond the field of AI—though often closely connected with it—developments in wearable devices have commandeered a significant part of the recent scientific literature, highlighting emerging new possibilities for the fuller monitoring and treatment of cardiovascular diseases and their related risk factors.

The technological developments in wearables—especially as they expand to cover not only the needs of fitness but also those of diagnosis and monitoring of cardiovascular diseases—will obviously require more substantial regulation to ensure device reliability, backed by well-organized studies that will highlight their cost-effectiveness so that insurance companies may be persuaded they should be reimbursable.