# Artificial Intelligence in Virtual Telemedicine Triage:
## 2 Related Work  
This section analyzes the literature about the use of artificial intelligence in healthcare, which is an area of increasing interest in recent years. Many studies have explored the potential of machine learning algorithms for medical diagnosis and prediction, including the use of deep learning methods for image and signal processing. Additionally, the development of telemedicine technology has provided new opportunities for remote diagnosis and monitoring of patients [5; 6]. Telemedicine can be used for the diagnosis and management of infectious diseases, including COVID-19. In particular, the use of remote monitoring devices for vital signs has been shown to be effective for the early detection of COVID-19 symptoms, allowing for timely intervention and treatment. Additionally, the use of machine learning algorithms has been explored for COVID-19 diagnosis, with some studies reporting high accuracy rates. For that reason, our analysis has focused mainly on machine learning algorithm solutions. [PERSON] et al. [16] presented a machine-learning-based forecasting model and mobile application for predicting susceptibility to COVID-19 using a health database. The project was implemented in a programming environment with Python and librariesinformation, in order to classify the danger of infections as low, medium, and high using a random forest model. The study found that the proposed approach using machine learning algorithms can accurately categorize the danger of infections as low, medium, and high with an accuracy of 90%. This means that the model can correctly predict the probability of being infected with the score of individuals with a high degree of accuracy. The precision rate of the model was found to be 96%, which indicates that the model can accurately identify individuals who are highly vulnerable to COVID-19 infection in a quarantine zone. Moreover, the sensitivity and F scores were also calculated to evaluate the performance of the model. The sensitivity rate was found to be 85%, which means that the model can correctly identify 85% of individuals who are actually at a higher risk of contracting COVID-19 infection. The F score, which is a measure of overall accuracy, was found to be 0.89, indicating that this approach has a high potential for public health decision making during the COVID-19 pandemic by providing precise estimates of individuals' infection severity for informed decision making and targeted response. Overall, these results suggest that this machine learning model and the mobile application has great potential for identifying individuals who are highly vulnerable to COVID-19 infection in a quarantine zone with high accuracy and precision. This approach can help healthcare professionals prioritize intensive care resources and plan more aggressive treatment for patients with a higher risk of infection while reducing the burden on healthcare systems. [PERSON] et al. [17] discussed a study that used machine learning to predict the diagnosis of COVID-19 using blood tests. The authors reviewed existing research on machine learning applications for COVID-19 diagnosis and identified limitations of the conventional RT-PCR test. For this reason, they used four distinct classifiers employed with a technique known as the synthetic minority oversampling technique (SMOTE) for classification. They also conducted exploratory data analysis to identify potential predictors of COVID-19 diagnosis based on demographic information, clinical symptoms, laboratory results, and RT-PCR test results. The results of the study showed that all four classifiers had high accuracy (above 90%) in predicting COVID-19 diagnosis based on blood test results. The authors also evaluated the performance of each classifier using various metrics such as accuracy, recall, specificity, sensitivity, F1 score, AUC, and Brier score. The study demonstrates that machine learning can be a useful tool for improving the accuracy of COVID-19 diagnosis using routine blood tests.