Cardiovascular diseases exert a significant burden on the healthcare system worldwide. This narrative literature review discusses the role of artificial intelligence (AI) in the field of cardiology. AI has the potential to assist healthcare professionals in several ways, such as diagnosing pathologies, guiding treatments, and monitoring patients, which can lead to improved patient outcomes and a more efficient healthcare system. Moreover, clinical decision support systems in cardiology have improved significantly over the past decade. The addition of AI to these clinical decision support systems can improve patient outcomes by processing large amounts of data, identifying subtle associations, and providing a timely, evidence-based recommendation to healthcare professionals. Lastly, the application of AI allows for personalized care by utilizing predictive models and generating patient-specific treatment plans. However, there are several challenges associated with the use of AI in healthcare. The application of AI in healthcare comes with significant cost and ethical considerations. Despite these challenges, AI will be an integral part of healthcare delivery in the near future, leading to personalized patient care, improved physician efficiency, and anticipated better outcomes.

Artificial intelligence (AI) and language models such as ChatGPT-4 (Generative Pretrained Transformer) have made tremendous advances recently and are rapidly transforming the landscape of medicine. Cardiology is among many of the specialties that utilize AI with the intention of improving patient care. Generative AI, with the use of its advanced machine learning algorithms, has the potential to diagnose heart disease and recommend management options suitable for the patient. This may lead to improved patient outcomes not only by recommending the best treatment plan but also by increasing physician efficiency. Language models could assist physicians with administrative tasks, allowing them to spend more time on patient care. However, there are several concerns with the use of AI and language models in the field of medicine. These technologies may not be the most up-to-date with the latest research and could provide outdated information, which may lead to an adverse event. Secondly, AI tools can be expensive, leading to increased healthcare costs and reduced accessibility to the general population. There is also concern about the loss of the human touch and empathy as AI becomes more mainstream. Healthcare professionals would need to be adequately trained to utilize these tools. While AI and language models have many beneficial traits, all healthcare providers need to be involved and aware of generative AI so as to assure its optimal use and mitigate any potential risks and challenges associated with its implementation. In this review, we discuss the various uses of language models in the field of cardiology.

Cardiovascular disease (CVD) is recognized as the leading cause of mortality throughout the world. About one-third of global mortality is attributable to CVD. In addition to clinical presentation, specific clinical exam findings can assist in treating and preventing CVD. CVD may initially manifest as pulmonary pathology, and thus, accurate cardiopulmonary auscultation is paramount to establishing accurate diagnosis. One of the most powerful tools available to physicians is the stethoscope. The stethoscope first emerged in the year 1818, invented by a French physician, René Laennec. Since then, the initial modest monaural wooden tube has evolved into a sophisticated digital device. This paper provides an analysis of the evolution of the stethoscope as well as highlights the advancement made by the modern digital stethoscope including the application of this tool in advancing care for patients suffering from CVD.

Diabetes insipidus, characterized by excretion of copious volumes of dilute urine, can be life-threatening if not properly diagnosed and managed. It can be caused by two fundamentally different defects: inadequate or impaired secretion of antidiuretic hormone (ADH) from the posterior pituitary gland (neurogenic or central diabetes insipidus) or impaired or insufficient renal response to ADH (nephrogenic diabetes insipidus). The distinction is essential for effective treatment.