# Artificial Intelligence in Virtual Telemedicine Triage:
## 6 Conclusions and Future Recommendation  
The performance tests confirmed the compliance of the artificial intelligence algorithm. The support vector machine model was the best option as it adequately handled the classes \"COVID-19\", \"COLD\", and \"RHINITIS\" with accuracies of 0.85, 0.88, and 1, respectively, where the best-performing class in the model was the class \"RHINITIS\". This results in an overall average accuracy of 0.91, making the model suitable for medical use within the guidelines established by the collaborating doctor. The web application called \"Baymax\" used to diagnose patients with COVID-19, the common cold, and allergic rhinitis, was hosted on the Google Cloud server so that users can access it from any device with an internet connection without requiring any prior installation. By using the Flask framework together with Bootstrap 5 we achieved a responsive, dynamic, and robust application, as it has security for both the user and the web application. In addition, the tools of HTML, CSS, JavaScript, Python, and MySQL used in the development of the project allow the web application to be scalable depending on the needs of the patients. One of the potential benefits of the application of artificial intelligence in telemedicine is the ability to broaden the scope of medical care through increased diagnostic capability. In this regard, one possible strategy to improve the performance of the telemedicine virtual assistant developed in this study is the incorporation of data from other diseases and symptoms. The inclusion of more data would allow the artificial intelligence algorithm to learn more complex patterns and expand its diagnostic capability, which could improve the accuracy and scope of patient classification. For example, if additional respiratory disease data were incorporated, the telemedicine virtual assistant could improve its ability to distinguish between different types of respiratory infections, such as pneumonia, bronchitis, or sinusitis. In addition, incorporating more data could also enable greater customization of the telemedicine virtual assistant for different clinical settings and patient populations. For example, if data from chronic diseases such as diabetes or hypertension were incorporated, the algorithm could be tailored to perform more precise monitoring of patients with these conditions. Overall, broadening the scope of the research project through the incorporation of more data from other diseases and symptoms could improve the accuracy and scope of the telemedicine virtual assistant developed in the study, allowing for greater diagnostic capabilities and more personalized medical care. However, it is important to keep in mind that the inclusion of additional data also requires careful assessment of the quality and relevance of the data, as well as the implementation of security and privacy measures to protect patient information. Conceptualization, N.V. and M.V.G. ; methodology, N.V.; software, N.V., S.M. and P.A. ; validation, M.V.G. ; investigation, N.V., P.A. and M.V.G. ; writing--original draft preparation, N.V. and M.V.G. ; writing--review and editing, P.A. and M.V.G. ; visualization, S.M. ; supervision, M.V.G. ; funding acquisition, S.M. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.