State of the Art Medical Large Language Models
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?”
Lessons Learned Applying Large Language Models
This session shares currently deployed software, lessons learned, and best practices that John Snow Labs has learned while enabling academic medical centers, pharmaceuticals, and health IT companies to build LLM-based solutions. It covers what generative AI use cases are healthcare organizations deploying today, which solution architectures are used to deliver these use cases, and general-purpose LLMs perform versus healthcare-specific LLMs in these use cases.
What’s in the Box
- Document Classification
- Entity Disambiguation
- Contextual Parsing
- Patient Risk Scoring
- Deep Sentence Detector
- Medical Spell Checking
- Medical Part of Speech
- Terminology Mapping
- Name Consistency
- Gender Consistency
- Age Group Consistency
- Format Consistency
- Entities by Prompt
- Relations by Prompt
- Classification by Prompt
- Relative Data Extraction
Language Models
Terminologies
2,000+ Pretrained Models
Signs, Symptoms, Treatments, Findings, Procedures, Drugs, Tests, Labs, Vitals, Sections, Adverse Effects, Risk Factors, Anatomy, Social Determinants, Vaccines, Demographics, Sensitive Data
Clinical Trial Design, Protocols, Objectives, Results; Research Summary & Outcomes; Organs, Cell Lines, Organisms, Tissues, Genes, Variants, Expressions, Chemicals, Phenotypes, Proteins, Pathogens