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Large Language Models Blog

Healthcare providers are increasingly required to adhere to guidelines and performance measures in order to comply with quality initiative, pay for performance, and payer standards. However, in practice many providers just don’t have the time to keep up to date with long and frequently changing guidelines, as well as matching the right next action to each complex patient.We introduce a novel solution that automatically:Answers detailed questions about clinical guideline documents, including understanding visual tables, flowcharts, and fine‑grained criteria.Matches a patient case, presented as an unstructured text summary, to the right guideline and the relevant section within that guideline for that specific patient right now.Explains its thinking and provides deep links into the relevant section of a guideline, that best answers a question or matches a given patient.The solution is the result of a partnership between Guidelines Central – which curates current, practical, and easily accessible guidelines coming from over thirty‑five medical societies and government agencies – and John Snow Labs – which provides state‑of‑the‑art Medical LLMs that have been specifically tuned for reasoning on clinical guidelines.This results in a turnkey solution, available either as a standalone tool for clinicians or medical societies, or as an embeddable module for EHR and CDS vendors, that can provide accurate, current, and actionable recommendations for the evidence‑based next‑best‑action for each patient – in one unified tool that covers multiple specialties and case settings.

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Healthcare providers are increasingly required to adhere to guidelines and performance measures in order to comply with quality initiative, pay for performance, and payer standards. However, in practice many providers just...

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Recent advancements in vision-language models (VLMs) have demonstrated remarkable capabilities across diverse domains. In this talk, we explore the effectiveness of VLMs in a transfer learning setting, where a pre-trained model...

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