Diagnostic Performance of Artificial Intelligence in Detecting COVID-19 Pneumonia on Chest Imaging.

Publication/Presentation Date

1-1-2026

Abstract

The COVID-19 pandemic highlighted the need for rapid, accurate, and accessible diagnostic tools. Chest imaging modalities, including chest radiography (CXR) and computed tomography (CT), provided valuable diagnostic information and prompted the development of artificial intelligence (AI) systems to support image interpretation and improve workflow efficiency. This literature review synthesizes current evidence on the diagnostic performance, limitations, and clinical implications of AI models in COVID-19 pneumonia detection through CXR and CT evaluation. A PubMed search was conducted through October 2025 to identify studies evaluating AI systems for the detection of COVID-19 pneumonia using CXR and CT. Studies reporting diagnostic performance metrics, including sensitivity, specificity, accuracy, or area under the curve (AUC), were included. Study quality and risk of bias were assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Eleven studies met the inclusion criteria. CXR-based AI systems demonstrated sensitivities from 80% to 98% and specificities from 82% to 96%, often comparable to radiologist performance. CT-based AI models achieved accuracies between 90% and 96%. AI models demonstrated strong internal diagnostic performance on CXR and CT but showed reduced accuracy with external validation, underscoring limitations related to generalizability and retrospective study designs. AI models demonstrate promising diagnostic performance for detecting COVID-19 pneumonia on chest imaging and may enhance radiologist efficiency. However, challenges related to generalizability, model adaptability, and clinician trust remain. Future research should prioritize external validation and transparent reporting to ensure the safe and effective integration of AI into clinical practice.

Volume

18

Issue

1

First Page

101775

Last Page

101775

ISSN

2168-8184

Disciplines

Medicine and Health Sciences

PubMedID

41710827

Department(s)

Medical Education

Document Type

Article

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