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State of the Art Generative AI for Healthcare

Build your private chat experience that explains its answers, shares no data, and is trained on your own data and tasks.

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Healthcare-specific LLM’s are More Accurate than General-purpose LLM’s

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.

Watch: Medical Large Language Models for Clinical Text Summarization, Information Extraction, and Question Answering

Large language models like GPT-4 and their open-source counterparts provide a leap in capabilities on understanding medical language and context – from passing the US medical licensing exam to summarizing clinical notes. Recently, a wave of health-specific large language models shows that tuning models specifically on medical data and tasks can result in even higher accuracy on everyday use cases such as question answering, information extraction, and summarization.

Ask Questions
that Medical Professionals Care about

Answering Clinical Questions

Answering Biomedical Questions

Train On Your Own Data

Load All Data: The Multi-Modal systems accepts structured data, unstructured text, PDF documents, and custom data formats.
Freshness: Always ensure your users get the latest answers. Update documents in near-real-time without retraining the whole system.
Scale: Ready to process millions or billions of documents. Scale the cluster to fit your needs.

Add Industry Data

Pre-load the knowledge base and pre-train the model with 2,300+ reference datasets curated by medical domain experts – including hundreds of terminologies, medical research, clinical trials, patents, population health, cost, public and regulatory data.