John Snow Labs Trainings & Certifications
Build Expertise in Clinical AI — Hands-On, Certified, Led by JSL Engineers
John Snow Labs offers instructor-led workshops designed for technical teams building and deploying healthcare AI in production. All workshops are led by current contributors to the John Snow Labs codebase, include hands-on access to live environments and temporary license keys, and end with a certification exam.
Medical Language Models for Data Scientists: Hands-On Generative AI for Healthcare
October 20–21, 2026 | 9:00 AM – 1:00 PM PST | Online
A two-day, hands-on workshop for data scientists who build on John Snow Labs' medical language models. Participants receive temporary license keys and run live Python notebooks on real clinical tasks.
Day 1 covers the core pipeline: de-identification and PHI removal across text, FHIR, PDF, and DICOM; clinical NER, assertion status, and relation extraction; entity resolution to SNOMED CT, RxNorm, LOINC, and ICD-10; and medical LLM use for clinical reasoning, summarization, and question answering. Participants work through the published, peer-reviewed benchmarks behind each model and learn how to evaluate them against general-purpose frontier models for specific tasks.
Day 2 covers visual language models - OCR and document understanding, DICOM and image de-identification, and multimodal extraction - with benchmarks. It then introduces the Generative AI Lab for annotation and regulatory-grade human-in-the-loop workflows, and closes with production deployment: SageMaker, Azure, Databricks, and Snowflake options; inference cost optimization; privacy and security; and scaling inside your own environment where data never leaves your control.
Patient Journey Intelligence: Building OMOP-Based AI Agents on Multimodal Clinical Data
October 22, 2026 | 9:00 AM – 1:00 PM PST | Online
A one-day workshop for architects, data engineers, and technical leaders. The morning covers the Patient Journey Intelligence platform architecture — how clinical notes, scanned PDFs, FHIR resources, DICOM images, lab feeds, and claims become a unified, de-identified OMOP CDM dataset — and data governance capabilities: confidence scoring, full provenance on every extracted fact, versioning, audit trails, role-based access, and human-in-the-loop overrides.
The afternoon is hands-on: participants ingest synthetic multimodal data, build cohorts with natural-language queries, explore complete patient timelines, and query OMOP data over SQL, REST, and MCP. The session closes with building clinical AI agents on the governed data foundation, inheriting compliance and governance by default.
Led by John Snow Labs product and engineering leaders. Ends with a certification exam.