The intersection of global broadband technology and miniaturized high-capability computing devices has led to a revolution in the delivery of healthcare and the birth of telemedicine and mobile health (mHealth). Rapid advances in handheld imaging devices with other mHealth devices such as smartphone apps and wearable devices are making great strides in the field of cardiovascular imaging like never before. Although these technologies offer a bright promise in cardiovascular imaging, it is far from straightforward. The massive data influx from telemedicine and mHealth including cardiovascular imaging supersedes the existing capabilities of current healthcare system and statistical software. Artificial intelligence with machine learning is the one and only way to navigate through this complex maze of the data influx through various approaches. Deep learning techniques are further expanding their role by image recognition and automated measurements. Artificial intelligence provides limitless opportunity to rigorously analyze data. As we move forward, the futures of mHealth, telemedicine and artificial intelligence are increasingly becoming intertwined to give rise to precision medicine.

Prosthetic valve endocarditis (PVE) after surgical aortic valve replacement and transcatheter aortic valve replacement (TAVR) carries significant morbidity/mortality. Our review aims to compare incidence, predisposing factors, microbiology, diagnosis, management, and outcomes of PVE in surgical aortic valve replacement/TAVR patients. We searched PubMed and Embase to identify published studies from January 1, 2015 to March 13, 2020. Key words were indexed for original reports, clinical studies, and reviews. Reports were evaluated by 2 authors against a priori inclusion/exclusion criteria. Studies were included if they reported incidence and outcomes related to surgical aortic valve replacement/TAVR PVE and excluded if they were published pre‐2015 or included a small population. We followed the Cochrane methodology and Preferred Reporting Items for Systematic Reviews and Meta‐Analyses guidelines for all stages of the design and implementation. Study quality was based on the Newcastle‐Ottawa Scale. Thirty‐three studies with 311 to 41 025 patients contained relevant information. The majority found no significant difference in incidence of surgical aortic valve replacement/TAVR PVE (reported as 0.3%–1.2% per patient‐year versus 0.6%–3.4%), but there were key differences in pathogenesis. TAVR has a specific set of infection risks related to entry site, procedure, and device, including nonstandardized protocols for infection control, valve crimping injury, paravalvular leak, neo‐leaflet stress, intact/calcified native leaflets, and intracardiac hardware. With the expansion of TAVR to lower risk and younger patients, a better understanding of pathogenesis, patient presentation, and guideline‐directed treatment is paramount. When operative intervention is necessary, mortality remains high at 20% to 30%. Unique TAVR infection risks present opportunities for PVE prevention, therefore, further investigation is imperative.

In this digital era, artificial intelligence (AI) is establishing a strong foothold in commercial industry and the field of technology. These effects are trickling into the healthcare industry, especially in the clinical arena of cardiology. Machine learning (ML) algorithms are making substantial progress in various subspecialties of cardiology. This will have a positive impact on patient care and move the field towards precision medicine. In this review article, we explore the progress of ML in cardiovascular imaging, electrophysiology, heart failure, and interventional cardiology.