RESUMEN
Gastroesophageal reflux disease (GERD) is a disorder that usually presents with heartburn. GERD is diagnosed clinically, but most patients are misdiagnosed due to atypical presentations. The increased use of artificial intelligence (AI) in healthcare has provided multiple ways of diagnosing and treating patients accurately. In this review, multiple studies in which AI models were used to diagnose GERD are discussed. According to the studies, using AI models helped to diagnose GERD in patients accurately. AI, although considered one of the most potent emerging aspects of medicine with its accuracy in patient diagnosis, presents limitations of its own, which explains why healthcare providers may hesitate to use AI in patient care. The challenges and limitations should be addressed before AI is fully incorporated into the healthcare system.
RESUMEN
The COVID-19 outbreak has emerged as one of the most profound medical events of the 21st century, leaving an indelible impact on a global scale. The widespread prevalence causing significant illness and death needs collaborative and inventive efforts to deal with this challenge. One of the particular subset of the general population that had endured a significant impact was the pregnant population. A key complication of pregnancy seen in patients with a COVID-19 infection was the increased risk of developing preeclampsia. The angiotensin-converting enzyme 2 (ACE2) receptor is an important part of the renin-angiotensin system, which has been implicated in the control of blood flow and also is a key receptor in the pathogenesis of the multitude of symptoms of COVID-19. This study aimed to evaluate the psychiatric, hematological, neurological, and social factors influenced by the COVID-19 virus and its subsequent effect on the development of preeclampsia. Increased rates of anxiety and depression were seen globally during the COVID-19 pandemic and due to the following physiological response of anxiety and depression, elevated blood pressure levels and development of preeclampsia were noted. Neurological factors such as the development of posterior reversible encephalopathy syndrome and its relationship between COVID-19 and preeclampsia were also strongly observed. The observation suggested biomarkers such as serum neurofilament light may be used as a screening tool to stratify the severity of preeclampsia. Hematological parameters observed were most notable for the presence of thrombocytopenia, which itself is a marker of the severity of preeclampsia. The numerous effects of COVID-19 on preeclampsia have proven to have a tremendous impact on the healthcare burden. Careful analysis and prevention strategies, if implemented, will contribute to reducing the morbidity and mortality of patients with preeclampsia and COVID-19 infections.
