Document Type : مقاله مروری نظام مند
Authors
1 PhD student,, Department of computer engineering, Shiraz Branch,Islamic Azad University, Shiraz, Iran
2 Assistant Professor,, Department of computer engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran
3 Assistant Professor, Department of Health Information Technology, Shahrood University of Medical Sciences, Shahrood, Iran
4 Associate Professor, Department of Computer Science, Shiraz Branch, Islamic Azad University, Shiraz, Iran
Abstract
**Introduction:** Early detection of COVID-19 plays a crucial role in reducing mortality. With its ability to analyze large volumes of medical data, artificial intelligence (AI) can aid in the rapid and accurate diagnosis of this disease. This review study aims to assess the performance of AI models in detecting COVID-19.
**Methods:** A systematic search was conducted in the PubMed, Scopus, and WOS databases, yielding 143 relevant articles. The studies were analyzed based on the type of input data, algorithms used, performance evaluation metrics, the presentation of clinical rules, and the clinical validation of the models. Additionally, 451 review and meta-analysis articles were examined to determine the extent to which these algorithms have been integrated into clinical guidelines.
**Results:** The input data in the articles consisted of clinical records, medical images, and audio processing. More than 96% of the algorithms were found to be black-box models, lacking clinical validation by specialists. However, the average performance of these models was reported to be above 90%. The review of meta-analysis articles revealed that none of the algorithms had undergone formal clinical evaluation; only their performance on available data was assessed.
**Conclusion:** The review of studies shows that AI performs well in COVID-19 detection but faces limitations, including lack of explainability, reliance on training data, and the absence of extensive clinical evaluations. The development of white-box models and conducting broad clinical studies are crucial for ensuring the widespread acceptance of these algorithms in clinical settings.
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