Why healthcare organizations processing billions of documents choose structured NLP over LLMs for entity extraction: 93% accuracy, deterministic outputs, and regulatory compliance at scale.
Large language models generate fluent clinical summaries and answer medical questions impressively. But when healthcare organizations need to extract structured data from millions of clinical notes with reproducible accuracy, regulatory...
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...
A single short GPT-4o query consumes 0.43 Wh of electricity. Scale that to 700 million queries per day, and the annual electricity consumption equals 35,000 U.S. residential households, or 50...
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....
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