Learn how specialized medical LLMs support tumor board decision-making with 93% extraction accuracy, NCCN guideline matching, and clinical trial criteria alignment from real oncology implementations.
Oncology has always depended on multidisciplinary tumor boards (MTBs) to interpret complex, multimodal data, such as pathology reports, radiology findings, genomic profiles, prior treatments, patient history, and synthesize this information...
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...
When Guidelines Central partnered with John Snow Labs to match patients with clinical guidelines from 35+ medical societies, the technical challenge was not generating recommendations. Large language models can produce...
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...
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