RadNet subsidiary DeepHealth and CARPL.ai will collaborate to develop an AI system designed to monitor and optimize imaging AI performance to improve clinical outcomes and operational efficiency and to accelerate adoption of AI.
Under the agreement, DeepHealth will embed CARPL.ai’s AI orchestration platform, which will enable the selection, implementation, and monitoring of AI models within DeepHealth’s cloud-native operating system, DeepHealth OS. The new system will automate the measurement and monitoring of performance and safety metrics, such as specificity, sensitivity, and data and model drift, DeepHealth said.
Ultimately, monitoring AI performance is essential for ensuring the reliability and accuracy of AI applications over time, company officials noted.
![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)
