





John Snow Labs Honored with the 2026 Frost & Sullivan Customer
State of the Art Medical Language Models
Putting Medical LLM to Work
Regulatory-Grade Clinical Text De-Identification
Corporate Vision
Free-Software
downloads of open-source libraries and AI models
enterprise customers including healthcare systems, pharma, payers, government, and IT
public case studies of real-world implementations
peer-reviewed papers and patents establishing state-of-the-art accuracy
Purpose built for scalable healthcare tasks such as information extraction, clinical summarization, reasoning, Q&A - #1 on 12 healthcare benchmarks vs. GPT-5.4, Gemini-3.1, and Claude-Opus-4.6.
3,000+ small language models for de-identification, NER, assertion, and relation extraction - fast and deployable on commodity hardware.
No-code platform for human-in-the-loop annotation and validation. Build regulatory-grade AI pipelines without writing a single line of code.
Anonymize free text, FHIR, PDF, and DICOM files with regulatory-grade accuracy. The most accurate clinical de-identification solution available.
Automate patient registries, cohorts, and quality measures from clinical documents - turning unstructured EHR text into structured, queryable data.
Secondary Use Platform that integrates multimodal, longitudinal clinical data into a unified, living OMOP — delivering a complete, structured view of every patient's care pathway.
Enterprise-grade AI governance, compliance, and risk management — ensuring healthcare AI deployments meet regulatory and audit requirements at scale
Martlet AI — HCC Coding AI-powered Hierarchical Condition Category coding that automates risk adjustment workflows with clinical-grade accuracy for health plans and providers.
Trends in Applied NLP in Healthcare: Large Language Models, No-Code, and Responsible AI
Large Language Models in Healthcare: Benchmarks, Applications, and Compliance
Advancing the patient and provider experience with Enterprise AI
Leveraging large language models for temporal relations extraction in oncological electronic health records.
Can Zero-Shot Commercial APIs Deliver Regulatory-Grade Clinical Text DeIdentification?
The Importance of Information Extraction from Unstructured Clinical Data in Pharmacoepidemiology
3,000+ pre-trained clinical models. Proven at 2 billion patient notes. Runs inside your secure environment