Diagnostic performance of artificial intelligence models for pulmonary nodule classification: a multi-model evaluation.

ABSTRACT:

Objectives

Lung cancer is the leading cause of cancer-related mortality. While early detection improves survival, distinguishing malignant from benign pulmonary nodules remains challenging. Artificial intelligence (AI) has been proposed to enhance diagnostic accuracy, but its clinical reliability is still under investigation. Here, we aimed to evaluate the diagnostic performance of AI models in classifying pulmonary nodules.

Materials and methods

This single-center retrospective study analyzed pulmonary nodules (4-30 mm) detected on CT scans, using three AI software models. Sensitivity, specificity, false-positive and false-negative rates were calculated. The diagnostic accuracy was assessed using the area under the receiver operating characteristic (ROC) curve (AUC), with h

SUBMITTER: Herber SK 

PROVIDER: S-EPMC12712079 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature