Medical software Healthcare LLM

clinicalAI

State of the Art Medical Large Language Models Clinical Note Summarization is 30% more accurate than BART, Flan-T5 and Pegasus. Clinical Entity Recognition John Snow Labs’ models make half the errors that ChatGPT does. Extracting ICD-10-CM Codes is done with a 76% success rate versus 26% for GPT-3.5 and 36% for GPT-4. Putting Healthcare LLMs to Production Use Using Healthcare-Specific LLM’s for Data Discovery from Patient Notes & Stories The US Department of Veterans Affairs, a health system which serves over 9 million veterans and their families. This collaboration with VA National Artificial Intelligence Institute (NAII), VA Innovations Unit (VAIU) and Office of Information Technology (OI&T) show that while out-of-the-box accuracy of current LLM’s on clinical notes is unacceptable, it can be significantly improved with pre-processing, for example by using John Snow Labs’ clinical text summarization models prior to feeding that as content to the LLM generative AI output. Text-Prompted Patient Cohort Retrieval: Leveraging Healthcare LLM Models for Precision Population Health Management Using John Snow Lab’s Healthcare LLM models, the ClosedLoop platform enables users to retrieve cohorts using free-text prompts. Examples include: “Which patients are in the top 5% of risk for an unplanned admission and have chronic kidney disease of stage 3 or higher?” or “Which patients are in the top 5% risk for an admission, older than 72, and have not undergone an annual wellness checkup?”