Background: Chest radiography is a fundamental imaging modality used to diagnose acute and chronic pulmonary diseases. It is relatively inexpensive, rapid, and can be performed at the bedside. Pulmonary abnormalities identified on chest radiographs are generally classified into two main categories: diffuse and focal. Depending on lesion size and image quality, abnormalities can be detected with an estimated accuracy of 70–85%. However, radiologists may overlook findings due to several factors, including heavy workloads, emergency conditions, and the presence of small focal lesions measuring less than 1 cm. Previous studies evaluating the application of artificial intelligence (AI) to chest radiographs for detecting lung cancer, COVID-19, and pneumonia have reported that the sensitivity of diagnostic interpretations improves from 88% to 95% when radiologists and AI work together. Aim: To evaluate selected thoracic abnormalities on chest radiographs using artificial intelligence and compare the results with radiologists’ interpretations. Materials and Methods: This retrospective study was conducted in the Department of Radiology at the Mongolia–Japan Hospital of the Mongolian National University of Medical Sciences. A total of 845 chest radiographs obtained between September 10 and September 30, 2022, were reviewed. After applying the exclusion criteria, 721 chest radiographs were included. The interpretations provided by four radiologists were compared with the results generated by an AI system. Results: A total of 721 posteroanterior chest radiographs were included in the study, comprising 377 images from male patients and 344 from female patients. By age group, the images included 24 from patients aged 10–19 years, 114 from those aged 20–29 years, 150 from those aged 30–39 years, 147 from those aged 40–49 years, 97 from those aged 50–59 years, 96 from those aged 60–69 years, 58 from those aged 70–79 years, and 37 from those aged 80–89 years. Radiologists identified abnormalities in 331 (46%) radiographs and reported no abnormalities in 390 (54%). In comparison, the AI system classified 392 (54%) radiographs as abnormal and 329 (46%) as normal. Using the radiologists’ interpretations as the reference standard, the AI system demonstrated a specificity of 95.8% and a sensitivity of 89% for detecting abnormalities on chest radiographs. Conclusion The AI system demonstrated high sensitivity and specificity in detecting abnormalities on chest radiographs, suggesting its potential utility in clinical practice. Its application may be particularly beneficial in supporting the interpretation of chest radiographs used for preventive screening and early detection of pulmonary abnormalities.