Application of Artificial Intelligence Methods Depending on the Tasks Solved during COVID-19 Pandemic

Abstract

Health systems challenges that emerged during the COVID-19 pandemic, such as a lack of resources and medical staff, are forcing solutions which optimize healthcare performance. One of the solutions is the development of clinical decision support systems (CDSS) based on artificial intelligence (AI). We classified AI-based clinical decision-supporting systems used during the pandemic and evaluated the mathematical algorithms present in these systems. Materials and methods: we searched for articles relevant to the aim of the study in the Scopus publication database. Results: depending on the purpose of the development a clinical decision support system based on artificial intelligence during pandemic, we identified three groups of tasks: organizational, scientific and diagnostic. Tasks such as predicting of pandemic parameters, searching of analogies in pandemic progression, prioritization of patients, use of telemedicine are solved for the purposes of healthcare organization. Artificial intelligence in drugs and vaccine development, alongside personalized treatment programs, apply to new scientific knowledge acquisition. Diagnostic tasks include the development of mathematical models for assessing COVID-19 outcomes, prediction of disease severity, analysis of factors influencing COVID-19 complications. Conclusion: artificial intelligence methods can be effectively implemented for decision support systems in solving tasks that face healthcare during pandemic.