In our previous article, JSL Vision: State-of-the-Art Document Understanding on Your Hardware, we benchmarked JSL Vision against leading open-source vision-language models on FUNSD and OmniOCR In this follow-up, we address the natural next question...
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,...
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