GE Healthcare and MiBA show how specialized healthcare NLP achieves 93-96% accuracy extracting radiology follow-ups, preventing lost patients at production scale.
When GE Healthcare's EDISON platform needed to transform radiology report processing for pharmaceutical partners, the engineering challenge extended far beyond extracting clinical findings from unstructured text. The real problem emerged...
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...
Visual de-identification is the process of detecting and masking protected health information (PHI) inside scanned medical documents, images, and PDFs. PHI in visual documents can appear in printed headers, stamps,...
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