Artificial Intelligence

CheXstray Drift Detection

CheXstray Diagram

A Stanford AIMI and Microsoft Healthcare team just took a step towards addressing imaging AI’s looming drift problem, unveiling their CheXstray drift detection system.

Imaging AI’s Drift Problem – The list of FDA-cleared imaging AI products continues to grow and we’re getting better at AI deployment. However, there’s no reasonable way to monitor how imaging AI models adapt to their constantly changing data environments (tech, vendors, protocols, patient & disease mix, etc.) or whether the models change on their own.

The CheXstray Solution – The team used a pair of public CXR datasets (n = 224k & 160k CXRs) to train/test the CheXstray solution to automatically detect drift by calculating a range of multi-modal inputs (DICOM metadata, image appearance, clinical workflows) and model performance. 

CheXstray Results – Initial experiments showed that the automated CheXstray workflows rivaled ground truth audits for drift detection, essentially achieving the workflow’s proof-of-concept goal. 

Automation Alternatives – Until we have automated monitoring solutions like CheXstray, AI vendors and radiology departments might have to rely on ongoing audits (requiring test set curation, labeling, analytics, etc.) and/or asking radiologists to provide ongoing model feedback. Unfortunately, those options undermine AI’s intended labor-reducing value proposition. Plus, radiologists have already made it quite clear that they don’t think monitoring should be their responsibility (and regulators might agree).

The Takeaway
We haven’t solved imaging AI’s drift monitoring problem yet, and there will be other hurdles to overcome before we see a solution like this achieve clinical adoption (more research, regulatory changes, new modalities, training without massive public datasets). Still, the CheXstray team just showed how imaging AI performance could be automatically monitored in real-time. That’s an important step in imaging AI’s evolution, and it might prove to be critical as more hospitals head into the 2nd or 3rd years after their “successful” AI deployments.

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