Non contrast CT brain (NCCTB) is commonly reported in teleradiology, requiring much radiologist time and with many factors contributing to reporting time. This analysis evaluates the impact of an AI algorithm, accounting for other known factors, on radiologist reporting time of NCCTB within a large teleradiology service.

A commercial comprehensive AI tool, detecting 130 findings on NCCTB, was deployed across a large Australian teleradiology service. Initially, the AI was deployed in the background to confirm successful technical deployment, with AI findings not made available to radiologists, followed by full launch where all radiologists were provided access to AI findings and the tool was adopted as standard practice. Deidentified details of reporting radiologist, study ID, AI findings, AI access per case, series count and system reporting timestamps were retrospectively collected across both periods for all standalone NCCTB reported between Feb 2022 and Jan 2023.

Reporting time was defined as the time between the “image-opened” and “dictation-end” timestamps. Data analysis was restricted to studies reported by radiologists whom reported a minimum of 50 studies with and without AI assistance. A linear mixed effects model was performed with reporting clinician as a random effect and reporting time as dependent variable, with a logtransform applied to estimate percent change in independent fixed variables. Time of day (in or out of hours), number of priority findings, worklist triage (critical, urgent or routine), series count and whether AI predictions were accessed, were evaluated as fixed effects.

A total of 18,550 studies reported by 30 radiologists were included in the analysis. Use of the AI and reporting out of hours was associated with an 8.5% and 7.1% reduction in reporting time respectively.

While the results of this analysis are limited to a small number of radiologists reporting solely in teleradiology, this analysis demonstrates that reporting time is influenced by a variety of factors. Reasonable efficiency gains in teleradiology reporting time were observed through the deployment of a comprehensive AI algorithm as standard practice, warranting further exploration of its impact on cost efficiency or other clinical workflow indicators, and in larger radiologist groups.

Even accounting for known factors which affect report time, the availability of a comprehensive AI tool to assist detection of findings has a striking association with reduced time to report non contrast CT brain cases, indicating high likelihood of improved clinical efficiency in the real world, teleradiology setting.