RapidAI Introduces EdgeIQ to Orchestrate Imaging AI Workflows

The system can initiate multiple analyses without waiting for every series within a study to be completed, while also following series-prioritization rules configured on scanners for acute examinations.

RapidAI has introduced EdgeIQ, an intelligent imaging orchestrator designed to automate the identification, prioritization, and routing of imaging studies across AI workflows. The technology is the latest component of the company’s Rapid Enterprise™ Platform.

According to RapidAI, EdgeIQ uses DICOM metadata and pixel data to identify relevant imaging series as they become available and route them to the appropriate AI modules. The system can initiate multiple analyses without waiting for every series within a study to be completed, while also following series-prioritization rules configured on scanners for acute examinations.

The platform is designed for health systems using multiple imaging AI algorithms, including third-party applications. It aims to standardize study matching and routing across sites with different scanners, protocols, and configurations, reducing reliance on manual protocol mapping and ongoing IT maintenance.

Unlike conventional routing systems that typically match a completed study with an AI algorithm, EdgeIQ operates continuously from image acquisition through processing. It can determine which series should be routed to specific AI modules, prioritize urgent cases, and retrieve relevant prior imaging for workflows requiring longitudinal comparisons.

EdgeIQ also supports the delivery of both positive and negative AI findings to designated clinicians or care teams. RapidAI said the system can help identify potential findings on scans performed for other clinical reasons and support AI workflows that compare current and prior imaging.

The platform uses a distributed architecture in which imaging data and DICOM headers are analyzed on-premises before relevant data is routed to cloud-based algorithms. Since imaging studies can exceed 1 GB, EdgeIQ sends only the data required by each AI module, which RapidAI said can reduce unnecessary data transfer, bandwidth use, and cloud storage requirements.

As imaging volumes and AI portfolios expand, automated study-to-algorithm matching is intended to reduce unprocessed examinations and the manual workload associated with maintaining imaging protocols.

Amit Phadnis, Chief Innovation and Technology Officer at RapidAI, announced EdgeIQ in a September 9, 2026 post.

Stay tuned for more such updates on Digital Health News