TL; DR This post presents a focused update on large-scale clinical de-identification benchmarks, emphasizing pipeline design, execution strategy, and infrastructure-aware performance. Rather than treating accuracy as an isolated metric, we...
Medical AI projects routinely deal with scanned documents and images that contain sensitive patient information. Extracting insights from these visuals is crucial – but so is protecting patient privacy. Traditionally,...
Doctors are taught a simple rule early in their training: When you hear hoofbeats, think horses, not zebras. In other words, the most common explanation is usually the right one....
How is AI improving hospital capacity forecasting? Hospital systems face constant pressure to balance patient demand with finite resources. AI-driven capacity forecasting provides hospitals with the predictive intelligence needed to...
TL;DR Summary As AI becomes embedded in clinical workflows, hospitals must transition from "trust by default" to a Zero Trust AI (ZTAI) architecture. This approach treats AI models and their...
Demos
See More