Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology.

Artificial intelligence (AI) and machine learning (ML) in medicine are currently areas of intense exploration, showing potential to automate human tasks and even perform tasks beyond human capabilities. Literacy and understanding of AI/ML methods are becoming increasingly important to researchers and clinicians. The first objective of this review is to provide the novice reader with literacy of AI/ML methods and provide a foundation for how one might conduct an ML study. We provide a technical overview of some of the most commonly used terms, techniques, and challenges in AI/ML studies, with reference to recent studies in cardiac electrophysiology to illustrate key points. The second objective of this review is to use examples from recent literature to discuss how AI and ML are changing clinical practice and research in cardiac electrophysiology, with emphasis on disease detection and diagnosis, prediction of patient outcomes, and novel characterization of disease. The final objective is to highlight important considerations and challenges for appropriate validation, adoption, and deployment of AI technologies into clinical practice.

What is the role of supplementation with ascorbic acid, zinc, vitamin D, or N-acetylcysteine for prevention or treatment of COVID-19?

Several agents intended to supplement dietary intake or endogenous molecules may have a theoretical role in preventing or treating COVID-19. Because of their potential to influence immune response, ascorbic acid (vitamin C), zinc, vitamin D, and N-acetylcysteine have been hypothesized to be useful for prevention or treatment of COVID-19. The authors outline the biologic plausibility, applicable clinical data, and potential role of each of these agents.