Artificial Intelligence Versus Anesthesiology Residents in Chest X-Ray Interpretation

This observational study will compare how accurately anesthesiology residents and an artificial intelligence system (hChestXR) identify abnormalities on chest X-rays. Approximately 35 volunteer anesthesiology residents at Kayseri City Hospital will review the same set of 150 anonymized posteroanterior chest radiographs selected from the hospital imaging archive. The set will include normal images and images showing findings such as infiltration, atelectasis, and pleural effusion. Two radiologists will independently assess the images and resolve disagreements by consensus to establish the reference standard. Residents will not see the radiologists' assessments or the artificial intelligence results while interpreting the images. The study will measure diagnostic accuracy and other measures of diagnostic performance. No additional imaging, treatment, or change in patient care is planned.

Inclusion Criteria: * An anesthesiology and reanimation resident working at Kayseri City Hospital. * Voluntarily agrees to participate in the study. * Provides written informed consent. * Agrees to evaluate the posteroanterior chest radiographs presented in the study. Exclusion Criteria: * Failure to complete the 150-radiograph assessment. * Withdrawal of consent before completion of the assessment.