Human-in-the-Loop (HITL) validation is critical to ensuring AI model outputs meet the highest standards of accuracy, compliance, and usability. Generative AI Lab is purpose-built to empower annotation teams to validate AI results efficiently and confidently. Our latest updates bring tangible improvements to how teams assign tasks, manage resources, and validate sensitive information—accelerating workflows, minimizing manual errors, and improving overall annotation quality.
Human-in-the-Loop (HITL) validation is critical to ensuring AI model outputs meet the highest standards of accuracy, compliance, and usability. Generative AI Lab is purpose-built to empower annotation teams to validate...
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...
In the era of large language models (LLMs)—where generative AI can write, summarize, translate, and even reason across complex documents—the function of data annotation has shifted dramatically. What was once...
Introduction Healthcare organizations are under increasing pressure to improve the accuracy and efficiency of clinical documentation and risk adjustment. With the rise of value-based care, Hierarchical Condition Category (HCC) coding...
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