Real-World Impact of an AI-Driven Teleradiology Workflow for Intracranial Hemorrhage Detection: Reducing Diagnostic Delays Across a Multicenter Emergency Network

Purpose Intracranial hemorrhage (ICH) is a time-sensitive emergency requiring rapid diagnosis. This study evaluated the real-world effectiveness of integrating an Artificial Intelligence (AI)-powered triage tool into a teleradiology workflow for detecting and prioritizing acute ICH on non-contrast CT (NCCT) scans, assessing the benefits on PACS-to-assessment time (PTAT) and report turnaround time (RTAT). Methods This retrospective, multi-vendor study compared NCCT interpretation for suspected acute ICH before (group1: pre-AI, June 2020) and after (group2: post-AI, June 2021) AI tool integration into a teleradiology network. In the post-AI phase, teleradiologists had access to the AI outputs. Diagnostic performance was assessed against a reference standard from two board-certified neuroradiologists. PTAT and RTAT were statistically compared between both phases, for all cases, ICH-positive findings and overnight cases. Results Among 572 patients (283 pre-AI, 289 post-AI), ICH prevalence was 12% in both groups. AI achieved 97.3% sensitivity, 96.0% specificity, and 99.6% negative predictive value. Mean PTAT and RTAT were significantly reduced by 13.72 minutes ( p  = 0.002) and 26.62 minutes ( p  = 0.001), respectively after AI integration. In ICH-positive cases, PTAT dropped by 31.43 minutes, though this did not reach statistical significance. During overnight hours, AI led to significant reduction of 26 minutes in both PTAT and RTAT (n = 131 pre-AI; n = 118 post-AI; p  = 0.001). Conclusion Integrating an AI-driven tool for the detection of ICH into a teleradiology workflow significantly reduced PTAT and RTAT without compromising diagnostic accuracy. This highlights AI’s potential to enhance teleradiology workflow efficiency, accelerate critical decisions, and improve patient outcomes in high-demand, resource-limited settings.