Impact of AI-Triaged Worklists and AI-Assisted Report Generation on Radiology Turnaround Times: Prospective Real-World Study

Background Radiology departments frequently manage large, heterogeneous worklists using first-in, first-out (FIFO) reporting workflows. This approach does not account for clinical urgency and may contribute to prolonged reporting delays, particularly in high-volume settings. AI systems are increasingly being integrated into radiology workflows, not only for image analysis but also as tools for worklist prioritization and report generation. However, real-world evidence of their operational impact remains limited. Objective This study aimed to evaluate the impact of an AI-triaged reading worklist combined with AI-assisted report generation on radiologist workflow efficiency, measured by report generation time (RGT) and overall turnaround time (TAT) for chest radiographs in a real-world hospital setting. Methods We conducted a single-center prospective paired study using a single-sequence crossover design. Eight board-certified radiologists interpreted chest radiographs during 2 reporting sessions: an unaided session using standard FIFO worklists and an AI-assisted session using an AI-triaged worklist with integrated report generation tools, separated by a 4-week washout period. Chest radiographs acquired between November 2023 and January 2024 were included. RGT was defined as the time from opening a study to report finalization, and TAT was defined as the time from the start of a reporting session to report finalization. Statistical comparisons were performed using nonparametric tests. Results A total of 1054 chest radiographs were included. Median RGT decreased from 2 (IQR 1-4) minutes in the unaided session to 0.53 (IQR 0.22-1.12) minutes in the AI-assisted session (P<.001), representing a 73.3% reduction. The largest reduction was observed in radiographs categorized as normal, with median RGT decreasing from 2 (IQR 1-3) minutes to 0.2 (IQR 0.13-0.35) minutes (a 90% reduction). Mean TAT decreased from 876.21 (SD 1014.20; 95% CI 816.06-940.55) minutes to 82.25 (SD 83.38; 95% CI 76.98-87.27) minutes with AI assistance, corresponding to a 90.6% reduction. Significant reductions in TAT were observed across all urgency categories, including critical studies (all P<.001). Conclusions In a real-world clinical setting, the use of AI-triaged worklists and AI-assisted report generation was associated with substantial reductions in RGT and overall TAT for chest radiographs. These findings suggest that AI, when deployed as workflow infrastructure rather than a diagnostic replacement, may meaningfully improve radiology operational efficiency and reporting timeliness.