The Impact of Artificial Intelligence-Assisted X-ray Interpretation on Radiologists' Diagnostic Performance

This study tests whether an artificial intelligence (AI) tool helps radiologists read X-rays more accurately. Radiologists read X-rays to find diseases such as pneumonia, bone fractures, and abdominal problems. AI is a computer program trained on many past X-rays. It may help radiologists spot diseases faster and more correctly. This study compares reading X-rays with the AI tool to reading X-rays without it. Who takes part * Ten radiologists from several hospitals. * X-ray images from about 300 to 500 past hospital visits. * No new X-rays are taken. * All names and ID numbers are removed from the images. What happens * Each radiologist reads the same set of X-rays twice. * One time, the radiologist reads without the AI tool. * The other time, the radiologist can see the AI result. * Half start with no AI and then switch to AI. The other half start with AI and then switch to no AI. * There is a four-week break between the two times. * The researchers measure how accurate each radiologist is. They also time how long each image takes. Possible benefits and risks * The radiologists may get no direct personal benefit. * The results may show whether AI can help doctors read X-rays better in the future. * This is a low-risk study. The AI tool never tells a patient a diagnosis directly. * A qualified radiologist always makes the final diagnosis. * Patient data is stored on a locked, encrypted server.

Inclusion Criteria: * Participants (radiologists): actively practicing radiologists at participating hospitals, stratified by hospital and professional seniority, trained for the study reading task, and willing to provide written informed consent. * Cases (X-ray images): adult patients aged 18 years or older who underwent X-ray imaging of the chest, abdomen, or lumbar spine for clinical indications. * Cases with complete basic clinical information. * Cases with an established gold-standard diagnosis, defined as the comprehensive clinical diagnosis or a consensus opinion from at least two senior radiologists (deputy chief physician or above). Exclusion Criteria: * Cases with poor image quality that prevents reliable interpretation. * Cases without a clear gold-standard diagnosis or with missing key clinical information. * Duplicate cases. * Cases in which the patient recently received radiotherapy, chemotherapy, or other treatment that would significantly alter imaging appearance and confound diagnosis. * Cases judged by the investigators as unsuitable for the study for any other reason. * Radiologists: no additional exclusion criteria beyond the inclusion requirements.