JMIR Publications

ABSTRACT

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. Artificial intelligence (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 on 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 using 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 two 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 the reporting session to report finalization. Statistical comparisons were performed using nonparametric tests.

Results:

A total of 1,054 chest radiographs were included. Median RGT decreased from 2.00 minutes (IQR 1.00–4.00) in the unaided session to 0.53 minutes (IQR 0.22–1.12) 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 2.00 minutes to 0.20 minutes (90% reduction). Mean TAT decreased from 876.21 minutes (95% CI 816.06–940.55) to 82.25 minutes (95% CI 76.98–87.27) with AI assistance, corresponding to a 90.6% reduction. Significant reductions in TAT were observed across all urgency categories, including critical studies.

Conclusions:

In a real-world clinical setting, the use of AI-triaged worklists and AI-assisted report generation was associated with substantial reductions in report generation time and overall turnaround time for chest radiographs. These findings suggest that AI, when deployed as workflow infrastructure rather than as a diagnostic replacement, may meaningfully improve radiology operational efficiency and reporting timeliness.