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

Authors

Srinath Sridharan, Nicholas Png, Alicia X H Seah, Narayan Venkataraman, Kelvin S H Sng, Wee Kiong Lim, Han Leong Goh, Yan Xian Goh, Andy Ta, Kang Min Wong, Weien Chow, Kee Chong Ng, Charlene Liew

Published in

Journal of medical Internet research. Volume 28. Pages e92181. Aug 31, 2026. Epub Aug 31, 2026.

Abstract

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.
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.
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.
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).
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.

PMID:
42673581
Bibliographic data and abstract were imported from PubMed on 01 Sep 2026.

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