RapidAI has introduced EdgeIQ, a new intelligent imaging orchestration system designed to automate how medical imaging studies are matched with AI models, prioritized and routed to clinicians across complex health system environments.
EdgeIQ is the newest component of the Rapid Enterprise Platform and is designed to address a growing challenge for hospitals deploying multiple imaging AI applications across different sites, scanners and protocols. Much of that work currently requires manual, case-by-case configuration and ongoing IT maintenance.
Rather than treating an imaging study as a single file, EdgeIQ recognizes that a study can include multiple series of images, with different AI algorithms requiring different combinations of those series.
Built on Rapid Edge Cloud, EdgeIQ uses DICOM metadata and pixel data to determine which image series are relevant as they become available and automatically sends them to the appropriate AI modules. For acute examinations, the system also respects prioritization rules already configured on the scanner.
This means AI analysis can begin before the entire imaging study has finished becoming available, potentially accelerating processing in time-sensitive clinical situations.
The platform is designed to support both RapidAI technology and third-party AI algorithms, providing health systems with a consistent framework for identifying, processing and prioritizing eligible medical imaging across multiple facilities even when scanner and protocol configurations differ.
One of EdgeIQ’s primary differentiators is that its orchestration begins during image acquisition rather than after a study is completed.
Traditional imaging routing generally functions as a handoff in which a finished study is matched with an algorithm. EdgeIQ continuously coordinates which series should be sent to which AI models, determines what should receive priority and can bring relevant historical imaging into the workflow when needed.
The approach enables multiple AI workflows to operate in parallel, supports urgent analysis and allows more complex comparisons between current and previous medical images.
EdgeIQ also works with Rapid Edge Cloud’s routing capabilities to send both positive and negative AI findings to care teams.
RapidAI noted that negative AI results can also provide clinically useful information. When considered alongside other patient information, a negative result could help clinicians rule out a suspected condition, refine a differential diagnosis or determine the next step in care.
By automatically identifying studies eligible for particular AI models, EdgeIQ is also designed to increase the likelihood that potentially important findings are surfaced, including incidental findings discovered on scans originally performed for another clinical reason.
Another capability involves longitudinal imaging.
EdgeIQ can automatically retrieve relevant prior studies and route the appropriate current and historical image series into AI workflows designed to measure changes over time. This can reduce the need for clinicians or other staff to manually search for and request earlier imaging before performing longitudinal analysis.
RapidAI also designed the platform around the large amounts of data associated with modern medical imaging.
EdgeIQ uses a distributed architecture that analyzes pixels and DICOM headers on premises to identify relevant image series. Cloud-based processing then applies anatomy recognition and algorithm-specific series-selection rules.
Because medical imaging studies can exceed 1 GB, EdgeIQ sends only the data required by each AI module to the cloud rather than transferring an entire study unnecessarily. RapidAI said this approach can reduce bandwidth consumption, cloud storage requirements and unnecessary data transfers while maintaining the low-latency performance required for acute care.
Automating the matching process is also intended to reduce the operational burden placed on imaging technologists and hospital IT teams as the number of scans and AI applications increases.
Results can still be configured around individual healthcare organizations’ workflows, allowing information to be routed to a specific specialist, an entire care team or another customized group.
RapidAI positions EdgeIQ as the foundational routing infrastructure for its Rapid Enterprise Platform.
The technology connects imaging acquisition, patient data, AI processing and clinical workflows so that AI capabilities can be deployed more consistently throughout a health system rather than requiring individual configurations at each location.
RapidAI said EdgeIQ will also provide the routing and prioritization foundation for additional capabilities planned for its platform, including upcoming functionality within Navigator Pro.
The company distinguishes EdgeIQ from broader AI orchestration platforms that primarily function as governance layers for managing algorithms from multiple vendors.
Instead, EdgeIQ operates closer to the point where medical images are acquired. It continuously determines which studies and individual image series need processing, which AI modules should receive them and which examinations should take priority.
The platform can also send different series from the same examination to multiple AI applications simultaneously, automatically account for scanner-level priorities and integrate earlier scans into workflows requiring comparisons over time.
RapidAI said the overall objective is to give health systems a scalable infrastructure layer that can consistently match the right imaging study with the right AI, route results and prior scans to the appropriate clinicians and prioritize the most urgent cases as healthcare organizations expand their use of imaging AI.
