AI-Based Quality Control for Radiology Reporting

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The goal of this clinical trial is to learn whether an artificial intelligence (AI)-based radiology report quality-control system can improve the accuracy and workflow efficiency of routine radiology reporting among active radiologists at Tongji Hospital. The main questions it aims to answer are: * Does access to the AI quality-control system reduce quality problems remaining in final radiology reports? * Does access to the AI quality-control system improve reporting and reviewing efficiency? * In the two-stage reporting workflow, do the effects of AI differ depending on whether AI is available to the reporting radiologist, the reviewing radiologist, or both? Researchers will compare radiologists assigned to receive access to Radiology Report GPT with radiologists who continue the usual reporting workflow without access to the study AI system. Radiologists are randomized at the individual level. Because CT and MR reports commonly involve both a reporting radiologist and a reviewing radiologist, report-level analyses will classify reports according to AI availability at the two workflow stages, resulting in four combinations of reporter and reviewer AI exposure. The primary randomized evaluation will cover the first month after intervention initiation, with prespecified secondary and exploratory analyses extending through 3 months. Participants will: * Be assigned to either the AI quality-control group or the usual-workflow control group. * Continue their routine radiology reporting and reviewing work during the study period. * If assigned to the AI group, receive access to Radiology Report GPT, which automatically pre-reviews submitted radiology reports and provides quality-control alerts and suggested revisions. Radiologists will retain final decision authority over all reports. Final-report accuracy will be assessed independently of the intervention AI system and will include human review. Additional prespecified outcomes will examine report revisions, AI interaction measures, stage-specific and cross-stage workflow effects, recommended imaging follow-up and other downstream health-care utilization, patient satisfaction, and referring-clinician satisfaction.