Industry Standards Hold The Key to AI Adoption in Enterprise Imaging
Imaging Industry Standards Enable AI Adoption
DICOMweb RESTful API
FHIR RESTful API
How Dicom Systems AI Conductor Enables AI Adoption
- The AI router receives DICOM images or other imaging-related objects and scans all metadata as they are coming in
- AI router flags any predetermined metadata signaling that a set of imaging objects are candidates for one or several AI algorithms, some of which may be on prem, and others in a cloud. For cloud-based algorithms, the AI router platform can be programmed to automatically de-identify pixel and metadata in order to maintain strict HIPAA compliance.
- The AI router signals to the appropriate docker that a study has been identified for further processing by the algorithm
- The AI router pushes metadata via QIDO-RS and pixel data via WADO-RS to the appropriate docker
- The docker analyzes the data and returns a result back to the AI router via STOW-RS
- The AI router receives the results from the docker, translates the result into the appropriate standard expected by the end-user system: DICOM-SR to PACS and/or dictation system, HL7 or FHIR to EHR/EMR, DICOM overlays to the PACS
What’s Next In AI Adoption
Enterprise imaging workflows, with or without AI, already demand substantial scalability, as data sets continue to grow in size and depth of complexity. As the imaging community embraces and integrates AI, a few factors will continue to influence the rate and success of adoption.
The classic elements of IT performance will continue to play an important role in replicability, scalability and performance. As Executive Vice President Florent Saint-Clair said, “We cannot outrun the laws of physics.” For AI algorithms to perform at their fullest potential, the IT infrastructure itself must be up to the correct standards. Storage speeds (I/Ops), available bandwidth, CPU power, and memory continue to be critical factors in how AI solutions can consistently perform under varying IT conditions.
AI vendors must collaborate with the enterprise imaging industry to deploy their algorithms in a way that does not impair other software within the ecosystem from functioning at a level of efficiency that has become the accepted norm in enterprise imaging. The demands of imaging workflows, augmented by the time-sensitive nature of diagnostics, point to industry standards as the surest way to address the performance and scalability expectations of imaging enterprises. A dockerization of AI will ensure the replicability of these processes.
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