Chervenkov L, Miteva DG, Velikova T. Utilizing artificial intelligence as an arbitrary tool in managing difficult COVID-19 cases in critical care medicine. World J Crit Care Med 2025; 14(3): 102808 [DOI: 10.5492/wjccm.v14.i3.102808]
Corresponding Author of This Article
Lyubomir Chervenkov, MD, PhD, Assistant Professor, Department of Diagnostic Imaging, Medical University Plovdiv, Bul. Vasil Aprilov 15A, Plovdiv 4000, Bulgaria. lyubo.ch@gmail.com
Research Domain of This Article
Critical Care Medicine
Article-Type of This Article
Opinion Review
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Lyubomir Chervenkov, Department of Diagnostic Imaging, Medical University Plovdiv, Plovdiv 4000, Bulgaria
Lyubomir Chervenkov, Research Complex for Translational Neuroscience, Medical University of Plovdiv, Plovdiv 4000, Bulgaria
Dimitrina Georgieva Miteva, Department of Genetics, Faculty of Biology, Sofia University St. Kliment Ohridski, Sofia 1164, Bulgaria
Dimitrina Georgieva Miteva, Tsvetelina Velikova, Medical Faculty, Sofia University St. Kliment Ohridski, Sofia 1407, Bulgaria
Author contributions: Chervenkov L and Miteva DG were involved equally in conceptualizing the idea and writing the draft; Velikova T wrote additional sections in the paper; Chervenkov L was responsible for critically revising the manuscript for relevant intellectual content; Velikova T was responsible for project administration and funding acquisition. All of the authors approved the final version of the paper prior to submission.
Supported by European Union-NextGenerationEU, Through The National Recovery and Resilience Plan of the Republic of Bulgaria, No. BG-RRP-2.004-0008.
Conflict-of-interest statement: The authors declare no conflict of interest.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Lyubomir Chervenkov, MD, PhD, Assistant Professor, Department of Diagnostic Imaging, Medical University Plovdiv, Bul. Vasil Aprilov 15A, Plovdiv 4000, Bulgaria. lyubo.ch@gmail.com
Received: October 31, 2024 Revised: March 19, 2025 Accepted: March 20, 2025 Published online: September 9, 2025 Processing time: 261 Days and 7.9 Hours
Core Tip
Core Tip: Coronavirus disease 2019 (COVID-19) presents a range of characteristic patterns and findings on computed tomography (CT) scans that reflect disease progression and severity. Accurate interpretation is crucial for patient management, yet this task is complicated by the variability in radiologists' experience and training. Standardizing CT reporting by grouping findings into distinct categories based on disease stage could improve consistency. However, variability and potential subjectivity persist, highlighting the need for artificial intelligence (AI) support in imaging diagnostics. AI can aid radiologists in achieving more accurate, objective interpretations by identifying, classifying, and quantifying changes, ultimately contributing to a more reliable and standardized approach to diagnosing and managing COVID-19 in critical care settings.
