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 These technologies may not be the most up-to-date with the latest research and could provide outdated information, which may lead to an adverse event.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.

Inflammatory bowel disease (IBD), Crohn’s disease and ulcerative colitis are chronic inflammatory disorders of the intestines. The underlying inflammation activates the coagulation cascade leading to an increased risk of developing arterial and venous thromboembolic events such as deep vein thrombosis and pulmonary embolism. Patients with IBD are at a 2–3-fold increased risk of developing thromboembolism. This risk increases in patients with active IBD disease, flare-ups, surgery, steroid treatment, and hospitalization. These complications are associated with significant morbidity and mortality making them important in clinical practice. Clinicians should consider the increased risk of thromboembolic events in patients with IBD and manage them with appropriate prophylaxis based on the risk. In this review, we discuss the literature associated with the pathophysiology of thromboembolism in patients with IBD, summarize the studies describing the various thromboembolic events, and the management of thromboembolism in patients with IBD.

Celiac disease (CD) is a common chronic inflammatory disorder occurring in genetically predisposed individuals secondary to gluten ingestion. CD usually presents with gastrointestinal symptoms such as pain, bloating, flatulence, and constipation or diarrhea. However, individuals can present in a nonclassical manner with only extraintestinal symptoms. The neurological manifestations of CD include ataxia, cognitive impairment, epilepsy, headache, and neuropathy. A lifelong gluten-free diet is the current recommended treatment for CD. This review discusses the relevant neurological manifestations associated with CD and the novel therapeutics. Further research is required to get a better understanding of the underlying pathophysiology of the neurological manifestations associated with CD. Clinicians should keep CD in the differential diagnosis in individuals presenting with neurological dysfunction of unknown cause.

Hypertension is a prominent cardiovascular risk factor, especially among patients with diabetes and obesity. Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are a class of drugs originally developed to improve glycemic control in patients with diabetes; however, these agonists have subsequently demonstrated additional cardioprotective effects, including modest reductions in blood pressure (BP). This literature review examines the various mechanisms through which GLP-1 RAs reduce BP, including weight loss, improved endothelial function, and renal sodium management. While GLP-1 RAs are less potent in BP reduction compared to conventional antihypertensive agents, the broader metabolic benefits of these agonists make this class of drug a valuable adjunct in managing hypertension, particularly in patients with metabolic syndrome. Nonetheless, further studies are needed to explore the long-term effects of BP and optimize patient selection for maximal cardiovascular benefit.

Medical students represent a significant part of the health-care community and are active members of the coronavirus disease 2019 (COVID-19) response. This study aimed to evaluate various factors associated with COVID-19 vaccine intention among medical students via an online anonymous survey. A total of 370 students completed the online survey, with 229 (61.89%) not vaccinated for COVID-19. Of students not yet vaccinated, 45 (19.65%) were unsure or did not intend to accept the vaccine, while 184 (80.35%) intend to be vaccinated within 6 months. Overall, female gender, health status, clinical science enrollment, and the practice of COVID-19 preventative behaviors significantly correlated with the intention to be vaccinated within 6 months. Greater perceived risk for contracting COVID-19, lesser beliefs that the COVID-19 vaccination trials were rushed, and greater beliefs that being vaccinated would help complete their medical education were uniquely associated with the intention to be vaccinated within 6 months. Collectively, this study identified several factors that influenced medical students' intention to receive the COVID-19 vaccination. This information may be used in future immunization strategies to increase the vaccination rates among this group of future medical professionals.