The key takeaway is that dataset shift is inevitable. New types of documents, evolving healthcare practices, and changing patient demographics will all contribute to it. But with the right monitoring strategy—keeping an eye on dataset distribution, entity distribution, and confidence distribution—you can stay ahead of these changes.
Understanding the Use Case The PHI detection solution we delivered to the customer is designed to identify sensitive information in clinical notes, ensuring privacy and compliance with healthcare standards. However,...
John Snow Labs’ small medical Language model (MedS) outperformed GPT-4o in factuality (by 5–10%), clinical relevance, and conciseness across tasks like summarization, information extraction, and biomedical Q/A, showcasing the impact...
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,...
Demos
See More