The growing demand for chest radiography in healthcare, combined with radiologist shortages and increasing workloads, underscores the need for innovative diagnostic support tools. This crossover study evaluates the effect of commercially available deep learning-based automatic detection software (DLAD) on radiologists’ diagnostic performance in chest X-ray (CXR) interpretation. Five radiologists independently assessed a dataset of 540 anonymized CXRs, both independently and with DLAD assistance, in two phases separated by a 30-day washout period. DLAD assistance significantly improved diagnostic performance, with overall sensitivity ( \(\text {Se}\) ) increased from 0.762 (95% CI: 0.705–0.811) to 0.911 (0.870–0.941, \(p<0.001\) ), while specificity ( \(\text {Sp}\) ) remained unchanged at 0.850 (0.805–0.887, \(p=0.331\) ). The positive predictive value ( \(\text {PPV}\) ) slightly improved from 0.810 (0.755–0.856) to 0.836 (0.788–0.876, \(p=0.331\) ), and the negative predictive value ( \(\text {NPV}\) ) increased from 0.810 (0.763–0.850) to 0.941 (0.882–0.947, \(p<0.001\) ). These improvements were consistent across radiologists, with notable reductions in false-negative rates. The findings emphasize DLAD’s potential to standardize diagnostic accuracy, enhance sensitivity, and support radiologists in chest X-ray interpretation. These results highlight the clinical value of AI-assisted workflows in improving detection rates while maintaining specificity.