The challenge of bringing guidelines to the bedside is not new. But the tools to meet it have evolved. It is time for guideline developers to move beyond dissemination and toward interaction—for guidance that is not only accessible, but context-aware, computable, and patient-specific. We believe that with the right foundation, the right models, and the right respect for clinical complexity, AI can help us finally close the gap between what we know and what we do.
It is 2:45 p.m. on a Tuesday in a busy emergency department. Dr. Reyes, a seasoned internist, has just been handed the chart for Mr. Thompson, a 68 year-old man...
Introduction In the evolving landscape of artificial intelligence for healthcare, John Snow Labs continues to demonstrate exceptional leadership with the release of the very first Medical Vision-Language Model (VLM). This model,...
Preventing the preventable: how smart AI systems can reduce claim denials The envelope arrived on a Friday afternoon. Elaine Carter had just returned from her second round of physical therapy,...
This document compares the core capabilities, strengths, and limitations of OpenAI’s large language models (LLMs) with John Snow Labs’ Medical Terminology Server (TS), focusing on terminology mapping use cases in healthcare...
Assertion status detection is critical in clinical NLP but often overlooked, leading to underperformance in commercial solutions like AWS Medical Comprehend, Azure AI Text Analytics, and GPT-4o. We developed advanced...
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