Annotation, training and models
Every radiology product says AI now, and almost none of them say which kind they mean. Ours is trained here, on radiology images, and it works on the clerical half of the job: what was scanned, what is written on the film, and what the radiologist just said. This page is the whole of it, including the parts that are unglamorous.
What runs today
None of these is a diagnosis. Each is something a person does by hand many times a day, and each one is measured on the live system rather than estimated.
A model we trained reads the image and says which body part and view it is, so the study files itself against the right procedure. It is the one doing the volume: about 140,000 predictions since late June at an average confidence of 0.89.
The letters a radiographer puts on a film, marking left or right and the projection. About 176,000 images have been read this way, and each signal carries the anatomy, view and side it found.
Dictation is transcribed on our server rather than in the browser, so it behaves the same on every machine and does not depend on what speech engine a device happens to have.
In the viewer you click inside a structure and it outlines it, Ctrl-click to exclude, then accept or reject. It runs on our server so the browser downloads nothing. It finds nothing on its own: it outlines what you point at.
Before a single image trains anything
The taxonomy is versioned, and each training run records the exact revision it used. Editing it changes the next run and can never rewrite what a published model was trained on.
The annotation workspace
Labelling radiology images usually means exporting them to a separate tool, which means copying patient data somewhere else and reconciling it afterwards. Here the workspace is the same viewer radiologists already use, reading from the same archive, so nothing is exported anywhere.
Reviewers work a queue of studies that have already been reported, so the labelling effort never delays a patient. What they draw is stored against the exact image and frame it belongs to.
An honest limit: there is no second-reader workflow here. Annotations are not independently adjudicated and no agreement between annotators is measured. If you need that, it is a conversation to have before you plan a project, not after.
How a model reaches a site
A trained model is stored with everything needed to judge it: accuracy per class, what each class was most often confused with, the training curves, and how long it takes on one image. It is created switched off, and stays off until somebody looks at those numbers and promotes it.
Once promoted, sites collect it themselves. Each installation checks for a newer version every few minutes and swaps it in memory, so nothing restarts and nobody visits. If a model arrives whose output does not line up with its own list of labels, it is refused outright and the previous model keeps serving, because a mislabelled answer is worse than an old one.
What we will not say
The accuracy figure on this page describes a filing task and was measured the way engineers measure such things. It is not evidence of how a model behaves on a population it has never seen, and we will not offer it as though it were.
The segmentation tool is a well-known general-purpose model, not one we trained and not one specialised for medicine. The transcription and the text reading are third-party models too. We build the pipeline around them and we say which parts are ours.
And nothing here writes a report. There was an experiment in that direction and it was deleted rather than shipped, which we think was the right call and would rather tell you than have you read it somewhere.
Straight answers