Change in the Proportion of Correct Interpretations of Pulmonary Opacities

This randomized crossover clinical trial will evaluate whether an artificial intelligence-based expert system improves the interpretation of chest radiographs by final-year medical students, rural physicians, and general practitioners with less than two years of clinical experience. Participants will interpret chest radiographs containing normal findings or pulmonary opacities classified as alveolar, interstitial, or mixed. Each participant will review the same set of radiographs twice: once without assistance from the expert system and once with assistance from the artificial intelligence system. The order of these two reading conditions will be randomly assigned, with a six-week interval between sessions to reduce memory effects. The main outcome will be the proportion of correct interpretations compared with a previously established reference standard based on radiologist interpretation supported by chest computed tomography findings. The study will also assess diagnostic confidence and agreement with the reference standard. This study does not involve treatment decisions or direct patient care. It is intended to determine whether artificial intelligence support may improve the accuracy and confidence of less-experienced physicians when interpreting pulmonary opacities on chest radiographs.

Inclusion Criteria: * Final-year undergraduate medical students. * Physicians completing mandatory rural or social service. * Licensed general practitioners with less than two years of clinical experience. * Willingness to participate as a chest radiograph reader. * Ability to complete both study reading sessions. Exclusion Criteria: * Corrected visual acuity worse than 20/60 in the better-seeing eye. * Visual-field restriction, impaired contrast perception, binocular vision abnormalities, or another visual condition that prevents adequate interpretation of chest radiographs. * Inability to complete the visual acuity assessment before the reading session.