Substantial milestones have been attained in the field of heart failure (HF) diagnostics and therapeutics in the past several years that have translated into decreased mortality but a paradoxical increase in HF-related hospitalizations. With increasing data digitalization and access, remote monitoring via wearables and implantables have the potential to transform ambulatory care workflow, with a particular focus on reducing HF hospitalizations. Additionally, artificial intelligence and machine learning (AI/ML) have been increasingly employed at multiple stages of healthcare due to their power in assimilating and integrating multidimensional multimodal data and the creation of accurate prediction models. With the ever-increasing troves of data, the implementation of AI/ML algorithms could help improve workflow and outcomes of HF patients, especially time series data collected via remote monitoring. In this review, we sought to describe the basics of AI/ML algorithms with a focus on time series forecasting and the current state of AI/ML within the context of wearable technology in HF, followed by a discussion of the present limitations, including data integration, privacy, and challenges specific to AI/ML application within healthcare.

Cardiovascular diseases (CVDs) carry significant morbidity and mortality and are associated with substantial economic burden on healthcare systems around the world. Coronary artery disease, as one disease entity under the CVDs umbrella, had a prevalence of 7.2% among adults in the United States and incurred a financial burden of 360 billion US dollars in the years 2016–2017. The introduction of artificial intelligence (AI) and machine learning over the last two decades has unlocked new dimensions in the field of cardiovascular medicine. From automatic interpretations of heart rhythm disorders via smartwatches, to assisting in complex decision-making, AI has quickly expanded its realms in medicine and has demonstrated itself as a promising tool in helping clinicians guide treatment decisions. Understanding complex genetic interactions and developing clinical risk prediction models, advanced cardiac imaging, and improving mortality outcomes are just a few areas where AI has been applied in the domain of coronary artery disease. Through this review, we sought to summarize the advances in AI relating to coronary artery disease, current limitations, and future perspectives.

The human epidermal growth factor receptors (HERs) are expressed abundantly in the human body. The tumorigenic potential of HER2/neu is linked to its overexpression, amplification or somatic mutation. The HER2 gene amplification leading to protein overexpression has been reported in 25–30% of breast cancers and 10–30% of gastric/gastroesophageal cancers. While HER2 is a well-documented predictive, prognostic, and therapeutic marker in breast and gastric/gastroesophageal cancers, its relevance has also been demonstrated in multiple other malignancies. In this article, we will conduct an extensive review of current data pertaining to HER2 amplification, overexpression, or mutation in cancers other than breast and gastric cancers.

Chronic lymphocytic leukemia (CLL) is the most common leukemia affecting the western adult population. While CLL is known to be a risk factor for morbidity and mortality from coronavirus disease 2019 (COVID-19), COVID-19 has not been shown to be a risk factor for the development of CLL. We report a case of a 55-year-old man who presented with COVID-19 pneumonia and developed overt CLL during hospitalization. Four other cases were culled from the literature. We discuss mechanistic possibilities for the unmasking of CLL in susceptible individuals with COVID-19.