AI clinical deployment contexts in radiology

Radiology ML models can be deployed in one of three clinical contexts: within the image acquisition hardware, within the data storage system aka PACS, or on the radiologist’s workstation.

First, models can come pre-bundled with hardware where they are guaranteed to work together smoothly. As such, they require no dedicated technical integration, sales, or contracts. They are vendor-specific and can’t be utilized elsewhere.

These models improve the functionality of the hardware. ML models on a CT scanner can help with image reconstruction on x-ray projection data, while models on an ultrasound station can help sonographers adjust the probe and optimize the image.

Hardware is often a long-term investment costing millions, while ML is a rapidly advancing field. ML models bundled with hardware are therefore likely to go out of date very quickly. It will be interesting to see how software updates to hardware are able to accommodate this.

Next, we have models that are automatically triggered as data makes its way into storage. These are likely vendor-agnostic, especially given the DICOM standard to which virtually all radiographic images adhere.

Background-running ML models often produce minimal friction and little to no interruptions to clinical workflow. Model-generated artifacts such as a presence/absence of a pathology or a segmentation mask can be saved alongside the images.

Examples include workflow optimization models that automatically triage and prioritize cases for subsequent reads by radiologists. Model predictions here act as a secondary check in parallel to the radiologist’s independent assessment.

Finally, we have models running on the radiologist’s workstation and are often manually triggered. Model predictions can be edited by users for a closed feedback loop where models are periodically retrained and hopefully improve over time.

The physician-facing nature of these models (whether in a hospital or through teleradiology) gives a good amount of control back to the user. As such, the model front-end and its accompanying UI/UX are of extreme importance.