Tl; DR: This post explains why specialized pretrained PHI pipelines are often the best starting point for data scientists working with clinical text. Instead of building a custom PHI system...
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,...
What are vision-language models and why do they matter for radiology? Vision-language models (VLMs) are emerging as the connective tissue in radiology workflows: combining imaging data, textual reports, prior studies,...
Why data de-identification is not optional in healthcare AI In healthcare AI, the cornerstone isn’t just smart models. It’s trusted data. Without rigorous de-identification and governance, any AI initiative risks...
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