| تعداد نشریات | 286 |
| تعداد نشریات سازمان | 84 |
| تعداد شماره‌ها | 3,032 |
| تعداد مقالات | 33,934 |
| تعداد مشاهده مقاله | 45,317,189 |
| تعداد دریافت فایل اصل مقاله | 63,977,194 |
Artificial Intelligence as a Promising Tool for Evaluating COVID-19 Severity | ||
| Archives of Razi Institute | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 18 مرداد 1404 اصل مقاله (171 K) | ||
| نوع مقاله: Letter to Editor | ||
| شناسه دیجیتال (DOI): 10.22092/ari.2025.369878.3707 | ||
| نویسندگان | ||
| Piruz Shadbash* 1، 2؛ Alireza Namazi3، 4 | ||
| 1Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran. | ||
| 2Department of Microbiology and Microbial Biotechnology, Faculty of Life Sciences and Biotechnology, Shahid Beheshti University, Tehran, Iran. | ||
| 3Department of Cell and Molecular Biology, School of Biology, College of Science, University of Tehran, Tehran, Iran. | ||
| 4Department of Computer Science, School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran. | ||
| چکیده | ||
| The COVID-19 pandemic has posed unprecedented challenges to global healthcare systems, especially in the timely assessment of disease severity and resource allocation. Traditional clinical and imaging markers, although useful, often lack the sensitivity and speed necessary for early and accurate patient classification. In this context, artificial intelligence (AI) has emerged as a transformative tool in assessing COVID-19 severity, aiding diagnosis, prognosis, and clinical decision-making. AI, particularly through machine learning (ML) and deep learning (DL) algorithms, can process extensive volumes of clinical, imaging, and laboratory data with remarkable speed and accuracy. For instance, convolutional neural networks (CNNs) have shown high accuracy in detecting COVID-19-related abnormalities in chest CT and X-ray images, often outperforming conventional radiological assessments in identifying ground-glass opacities and fixation patterns. Additionally, AI models that integrate vital signs, oxygen saturation, comorbidities, and biomarkers have shown promise in predicting disease progression and risk of ICU admission. One notable application is the development of AI-based triage tools in emergency department, that can quickly identify high-risk patients and prioritize care, particularly when healthcare resources are limited. However, the pandemic has catalyzed the acceptance and adoption of AI in clinical medicine. Future strategies should concentrate on creating ethically sound, clinically validated, and interpretable AI systems tailored for pandemic response. Integrating real-time data from wearable devices, electronic health records, and cloud-based platforms can increase the capacity of AI to provide timely and accurate assessments of COVID-19 severity. We mentioned some AI software and their base fundaments and say how can we use of this ML datasets, how we can improve them with NPL and give some worldwide examples in global health care system like CheXNet and IBM Watson Health. In the following we talked about purposes and steps of using some popular AI applications in medical society and their potential users, to instance SOFA and KATE models that use in some sophisticated hospitals. In conclusion, AI shows a powerful complement to the fight against COVID-19, providing tools to accurately evaluate severity and optimize resources. Continued investment in AI research and its responsible implementation critical to strengthening global preparedness for current and future pandemics. | ||
| کلیدواژه‌ها | ||
| Artificial Intelligence؛ COVID-19؛ Pandemics؛ Machine Learning؛ Deep Learning | ||
|
آمار تعداد مشاهده مقاله: 2 تعداد دریافت فایل اصل مقاله: 1 |
||