In the last post we highlighted how AI medical imaging solutions can serve as ‘a second set of eyes’ reducing the risk of overlooked findings.

Operating in near-real time, they can affect patient treatment decisions, mitigating the risk of missed potentially life-threatening pathologies or, for instance, improving early stage cancer diagnostics.

These results are taken from a 2 year backlog analysis of chest X-rays at a tertiary hospital. 

Backlog analysis allows to adjust ChestEye performance to the data of the particular medical institution and establish an overall error threshold. Call it a supercharged audit, after which the institution can move into a real-time AI deployment framework.

Most importantly, this deployment requires little change to the workflow of the radiology department. Yet it can have a significant impact in diagnostic quality.