# Artificial Intelligence in Virtual Telemedicine Triage:
## 2 Related Work
However, it is important to note that this approach should not replace other diagnostic methods such as RT-PCR testing and clinical evaluation by healthcare professionals. In addition, they mention that future studies could explore other types of data, such as CT scans or cough sounds, to improve COVID-19 diagnosis using machine learning. [PERSON] et al. [18] developed an AI-based personal medical assistant, this application aims to offer intelligent healthcare and make it more approachable by analyzing symptoms, diagnosing and predicting health conditions, providing recommendations and treatment options based on user responses, and tracking users' health progress. Real-world cases with large amounts of clinical data, discharge summaries, prescriptions, and more are used to train the system through NLP information extraction. The app combines data on different fitness activities tracked, along with factors such as age, gender, location, past medical records, and calorie intake to generate a more precise analysis. The system is supported by an advanced AI engine that employs the naive Bayes classifier and other techniques, combined with a vast medical knowledge base comprising conditions, symptoms, findings, and treatments. The application also features a voice assistant with NLU capabilities, enabling it to understand human voice responses in addition to textual input. Overall, IntelliDoctor has the potential to revolutionize healthcare by providing personalized medical assistance to users anytime anywhere. However, it should be noted that while IntelliDoctor may be useful as a complementary tool for healthcare professionals or as a first point of contact for minor health issues or concerns, it should not replace professional medical advice or treatment in serious cases. [PERSON] et al. [19] analyzed more than 18,000 positive case records and used hierarchical clustering (HC) to group patients according to their clinical manifestations. To identify the best settings for clustering analysis, they conducted three primary stages:  
1. They assessed five diverse distance metrics (Binary, Manhattan, Euclidean, Minkowski, and Maximum) to ascertain the degree of variation in clinical presentations across all patients. The preferred metric was used to distinguish a distinct cluster for \"asymptomatic cases\". 2. They utilized the elbow method to ascertain the estimated number of primary clusters by graphing the explained variance against the number of clusters. 3. They used the HC to group patients according to their clinical manifestations and identified seven distinct clinical profiles with specific symptom compositions and correlations with viral load. The authors recognized that this study was an observational inquiry with sporadic sampling of suspected cases at various time intervals, most of whom were symptomatic with the ongoing disease and provided self-reported data on their symptoms. However, they believe that their findings provide valuable information on the clinical presentations of COVID-19 in Costa Rica and may have implications for public health interventions aimed at controlling the spread of the virus. [PERSON] et al. [20] proposed incremental learning, which is based on a dynamic ensemble method called the bagging scheme. This method consists of training multiple independent machine learning models and combining their predictions to obtain a more accurate final prediction. In addition, the bagging scheme allows for the addition of new models or the updating of existing models as new data are collected. To apply this approach to the COVID-19 context, the authors used mathematical modeling, simulation, and artificial intelligence techniques to collect and analyze data related to the SARS-CoV-2 disease. In particular, they used epidemiological and clinical data to build predictive models using SEIRD modeling (susceptible, exposed, infected, recovered, and killed) variables that characterize the spread of the virus.