John Snow Labs' benchmarking results reveal a significant leap in accuracy when compared to general-purpose LLMs like BART, Flan-T5, Pegasus, ChatGPT, and GPT-4. The findings demonstrate that John Snow Labs' models outperform these state-of-the-art LLMs in crucial tasks such as clinical note summarization, clinical entity recognition, medical data de-identification, and extracting ICD-10-CM codes.
Many data scientists have asked what and when to use AWS Medical Comprehend vs. John Snow Labs Healthcare NL. In this blog, we explore the aspects and differentiators of John...
Generative AI in healthcare is a transformative technology that utilizes advanced algorithms to synthesize and analyze medical data, facilitating personalized and efficient patient care. In healthcare, Generative AI has shown...
In this blog post, we spotlight the potential of smaller, specialized language models within a Retrieval-Augmented Generation (RAG) framework — a space where large LLMs like GPT-4o are commonly used....
This blog post explores how John Snow Labs’ Healthcare NLP & LLM library is transforming clinical trials by using advanced NER models to efficiently filter through large datasets of patient...
Healthcare-specific language models, like the JSL-MedS-NER family, are designed to extract clinical entities from unstructured medical text. These models can identify key information such as clinical terms, drugs, side effect,...
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