Following a successful trial, Aidoc has filed a submission with the U.S. Food and Drug Administration (FDA) for its breakthrough-designated multitriage device.
The triage system, which is powered by Aidoc’s Clinical AI Reasoning Engine (CARE) Foundation Model, identifies cases with acute, time-sensitive abnormalities immediately after scan completion. Trained on multimodal data, CARE is designed to detect and prioritize abdominal conditions.
In the study, the multitriage device demonstrated 97% sensitivity (up to 98.5% at maximum sensitivity) and 98% specificity (up to 99.7% at maximum specificity) across a wide range of abdominal findings.
Aidoc also noted that its aiOS operating system has attained the milestone of analyzing 100 million patient cases. Key attributes of Aidoc aiOS include precision orchestration tailored to hospital, modality, and physician workflow; continuous drift monitoring; and real-time multimodal data integration.
![A normal mammogram confirmed by three-year radiologic follow-up illustrates reader-marked regions of interest (ROIs) during (A) unaided (round 1) and (B) artificial intelligence (AI)–assisted (round 2) reading. Each colored dot represents an ROI for recall by a human reader. Readers could mark more than one ROI per case, represented by multiple dots of the same color. During AI-assisted reading, the AI system displayed three visible prompts: two with suspicion of malignancy scores of 35% (left mediolateral oblique [L MLO] and craniocaudal [L CC]) and one with a suspicion of malignancy score of 10% (right craniocaudal [R CC]), shown as polygonal overlays. Without AI, six of 10 readers (60%) marked a false-positive ROI. With AI assistance, this fell to two of 10 (20%). R MLO = right mediolateral oblique.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/07/2026-07-14-radiology-mammogram-ai-auto-bias.H0bYO8QlWs.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
