Artificial Intelligence Latency, Report Turnaround Time, and Adoption in a Multivendor Artificial Intelligence Ecosystem: A Multisite Observational Study.

Abstract

Objective: To evaluate infrastructure latency, workflow, and radiologist sentiment across a 4.5-year, multivendor artificial intelligence (AI) implementation program in a 20-center outpatient radiology network.

Methods: Three retrospective cohorts: a technical cohort (96,874 examinations; Sep 2023-Sep 2025) for PACS-to-PACS latency of 9 AI tools and temporal alignment of 10 AI tools; a report turnaround time (TAT) analysis cohort (20,909 examinations; March to August 2025) comparing TAT between AI-available and concurrent non-AI workflows by Mann-Whitney U; and a survey cohort (58 radiologists; two waves 2025) for adoption, Likert-scale perceptions, and Net Promoter Score.

Results: Active AI adoption was 91.4% (53 of 58), with 66% (35 of 53) reporting regular use. Median total latency was 2.06 min (IQR 1.74-3.05), 72% of which attributable to data routing. The "Too Late" rate (AI result arriving after report finalization) was 7.2% overall, ranging from 3.0% for knee MRI to 13.2% for chest CT. After adjustment for radiologist (linear mixed-effects models), AI availability was associated with lower median TAT for trauma radiography (-26%) and knee MRI (-18%; both P < .001); brain volumetry MRI showed no significant change (+9.2%; P = .33). In exploratory analysis, Net Promoter Score declined for chest CT (+38 to -3) and aorta CT (+22 to -25), nominally significant before multiple-comparison correction.

Discussion: Multivendor AI at scale was associated with measurable TAT gains in high-volume modalities. Infrastructure latency, not algorithm speed, was the primary barrier to clinical utility. At ∼23% to 25% annualized cost of one radiologist full-time equivalent (FTE) salary, the program generated 0.69 FTE of capacity through trauma radiography alone (0.46 FTE after radiologist-adjusted sensitivity analysis).