Computerized Intelligence and Mathematical Models for COVID-19 Diagnosis: A Review

Background: The COVID-19 infection, with its unknown aspects, has posed numerous challenges to public health systems worldwide, rapidly disseminating across borders. In the context of diagnosis, researchers are endeavoring to enhance diagnosis accuracy through improved decision-making processes. The present study aimed to conduct a comprehensive review of the benefits of using computerized intelligence and mathematical models for diagnosing COVID-19 infections. Methods: We searched for relevant references on the PubMed and Google Scholar databases, with inclusion criteria and search strategies utilized to identify full-text articles in English. A narrative report of our findings was presented based on the synthesis of our data. Result: The advantageous application of computerized intelligence has been approved in various medical domains, including prevention, diagnosis, and risk assessment. Conclusion: Collaborative efforts are anticipated to enhance pandemic control with increased precision and reduced costs.