John Snow Labs

John Snow Labs Helping healthcare and life science organizations put AI to work faster with state-of-the-art LLM & NLP.

John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects. John Snow Labs, the AI for healthcare company, provides state-of-the-art software, language models, and data to help healthcare and life science organizations build, deploy, and operate AI, LLM, and NLP projects faster.

How can healthcare organizations reliably digitize millions of handwritten pharmacy records without compromising accurac...
04/08/2026

How can healthcare organizations reliably digitize millions of handwritten pharmacy records without compromising accuracy or compliance?

Nima Babazadeh — Enterprise Data and AI Architect, EBOS Group and Jiri Dobes — Head of Solutions, John Snow Labs will present:

High-Accuracy Digitization for Millions of Handwritten Pharmacy Records Using Medical Visual Language Models
Applied Healthcare AI Summit — April 14–15 | Free Online

Processing handwritten pharmacy charts at scale presents unique challenges: diverse handwriting styles, complex form layouts, and the need for clinical-grade validation. Traditional cloud AI solutions often fail due to insufficient accuracy, high costs, and strict data residency requirements. In this session, Nima and Jiri will share how EBOS and John Snow Labs built a production-grade, Databricks-native pipeline that moves beyond experimental AI to operational reliability.

Attendees will learn how to:
• Apply multimodal healthcare AI to extract precise data from complex medical documents
• Ensure deterministic reliability with Small Language Models (SLMs) for consistent clinical entity recognition
• Deploy AI pipelines in sovereign infrastructure while maintaining auditability and governance
• Optimize compute resources to balance performance, cost, and clinical latency

Key insights:
• Leveraging Medical VLLM, Visual NLP, and Healthcare NLP for high-fidelity data extraction
• Designing pipelines with reproducible and deterministic outputs for regulated workflows
• Implementing end-to-end governance with Unity Catalog for lineage and access control
• Practical strategies for scaling multimodal AI in real-world pharmaceutical operations

Nima and Jiri bring hands-on expertise in operationalizing AI in high-stakes healthcare environments, combining enterprise data architecture with production-grade AI solutions.

👉 Register and explore the full agenda: https://hubs.li/Q049-DRT0
📅 April 14–15 | Free Online


Register instantly via LinkedIn: https://hubs.li/Q049-JwC0

As AI becomes a primary channel for health information, the challenge is no longer content generation. The real barrier ...
04/08/2026

As AI becomes a primary channel for health information, the challenge is no longer content generation. The real barrier is governance, how to verify that AI-produced messaging is accurate, safe, and aligned with public health standards before it reaches the public.

Brinleigh Murphy-Reuter, Founder & CEO at Science To People, and Anju Aggarwal, Head of Solutions at Pacific AI, will address this critical gap in their session:

Engineering Trust: A Governance Framework for Science Communication AI

📍 Applied Healthcare AI Summit, April 14–15 | Free Online

This keynote presents a real-world case study on building verification infrastructure for science communication AI through the development of VeriSciLM, a certified framework designed to operationalize trustworthy AI messaging at scale.

Rather than relying on generic accuracy checks, the session demonstrates how governance-by-design transforms AI from a content generator into a regulated communication system suitable for public health deployment.

What attendees will learn

• Designing AI governance aligned with ISO/IEC 42005, NIST AI RMF, CHAI, and NAM healthcare standards
• Expert-led red teaming and bias testing tailored to science communication
• Embedding automated safety guardrails directly into CI/CD pipelines
• Creating transparent, audit-ready Model Cards backed by healthcare benchmarks
• Building trust infrastructure connecting model capability to real-world deployment

The session shows how integrated evaluation, continuous monitoring, and standardized transparency create a reproducible certification pathway for AI used in public health messaging, creator platforms, and digital health ecosystems.

👉 Register and explore the full agenda: https://hubs.li/Q049-LzH0
📅 April 14–15 | Free Online

Quick registration via LinkedIn: https://hubs.li/Q049-LQz0

The keynote lineup for the Applied Healthcare AI Summit reflects where healthcare AI is heading next. Featured keynote s...
04/08/2026

The keynote lineup for the Applied Healthcare AI Summit reflects where healthcare AI is heading next.

Featured keynote sessions include:

• David Talby — John Snow Labs — Solving the Grand Challenges of Healthcare AI: The Trust Stack for the Regulatory-Grade Era
• Veysel Kocaman — John Snow Labs — Building Autonomous Agentic Workflows with the Patient Journey Intelligence Platform
• Suhana Bedi and Miguel Fuentes — Stanford University — MedHELM and the Next Phase of Open Medical AI Evaluation
• Bhavuk Jain — Google — AI-Augmented Diagnostics: Where Are We Actually Saving Lives?
• Brinleigh Murphy-Reuter (Science 2 People) and Anju Aggarwal (Pacific AI) — Engineering Trust: A Governance Framework for Science Communication AI
• Dia Trambitas — John Snow Labs — Proving Regulatory-Grade Accuracy and Provenance in Automated Cancer Registries
• Julio Bonis — Pacific AI — From Guardrails to Guardians: Continuous Red Teaming and Holistic Safety for Agentic Healthcare AI
• Aaron Neiderhiser — Tuva Project — Democratizing High-Quality Healthcare Analytics with the Open-Source Tuva Project
• Ritwik Jain and Hasham Ul Haq — Martlet AI — Proving Regulatory-Grade Accuracy in AI-Driven HCC Coding
• Nila Bhakuni (Baptist Health South Florida) and Linda Chen (John Snow Labs) - Unlocking Multimodal Data for Secondary Use: How Baptist Health Powers Discovery with the Patient Journey Intelligence Platform

Learn from leaders defining the future of applied healthcare AI.

View the full agenda: https://hubs.li/Q049-HWY0

Quick registration via LinkedIn: https://hubs.li/Q049-J560

One week until the Applied Healthcare AI Summit begins. Healthcare AI is entering a new phase, where trust, evaluation, ...
04/07/2026

One week until the Applied Healthcare AI Summit begins.

Healthcare AI is entering a new phase, where trust, evaluation, and production deployment matter as much as innovation.

This year’s summit brings together leaders from John Snow Labs, Stanford University, Google, Pacific AI, Science 2 People, Tuva Project, Martlet AI, and more to share real-world lessons from deploying AI in healthcare.

Across two days, sessions will explore:

• Regulatory-grade AI systems
• Autonomous agentic workflows
• Clinical evaluation frameworks
• Responsible deployment at scale

Join the global community building applied healthcare AI.

Register free: https://hubs.li/Q049VYHH0

Quick registration via LinkedIn: https://hubs.li/Q049VQYc0

Here’s a look at the keynote sessions coming to the Applied Healthcare AI Summit 2026: • David Talby — John Snow Labs — ...
04/03/2026

Here’s a look at the keynote sessions coming to the Applied Healthcare AI Summit 2026:

• David Talby — John Snow Labs — Solving the Grand Challenges of Healthcare AI: The Trust Stack for the Regulatory-Grade Era
• Veysel Kocaman — John Snow Labs — Building Autonomous Agentic Workflows with the Patient Journey Intelligence Platform
• Suhana Bedi and Miguel Fuentes — Stanford University — MedHELM and the Next Phase of Open Medical AI Evaluation
• Bhavuk Jain — Google — AI-Augmented Diagnostics: Where Are We Actually Saving Lives?
• Brinleigh Murphy-Reuter (Science 2 People) and Anju Aggarwal (Pacific AI) — Engineering Trust: A Governance Framework for Science Communication AI
• Dia Trambitas — John Snow Labs — Proving Regulatory-Grade Accuracy and Provenance in Automated Cancer Registries
• Julio Bonis — Pacific AI — From Guardrails to Guardians: Continuous Red Teaming and Holistic Safety for Agentic Healthcare AI
• Aaron Neiderhiser — Tuva Project — Democratizing High-Quality Healthcare Analytics with the Open-Source Tuva Project
• Ritwik Jain and Hasham Ul Haq — Martlet AI — Proving Regulatory-Grade Accuracy in AI-Driven HCC Coding
• Nila Bhakuni (Baptist Health South Florida) and Linda Chen (John Snow Labs) — Unlocking Multimodal Data for Secondary Use: How Baptist Health Powers Discovery with the Patient Journey Intelligence Platform

Across the summit, speakers from leading organizations including Google, Genentech, Medtronic, Abbott, Novo Nordisk, Vizient, NBME, and major academic medical centers will share how AI is moving from research environments into real clinical operations.

Expect perspectives on:

• Production deployment of LLMs
• Responsible AI governance frameworks
• Multimodal clinical intelligence
• Enterprise adoption of healthcare AI

Don’t miss this diverse set of voices shaping the next phase of applied healthcare AI.

Explore the full agenda: https://hubs.li/Q049yZmn0
Register instantly via LinkedIn: https://hubs.li/Q049yYnP0

𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝗔𝗰𝗰𝘂𝗿𝗮𝘁𝗲 𝗦𝗲𝗮𝗿𝗰𝗵 𝗶𝗻 𝗠𝗲𝗱𝗶𝗰𝗮𝗹 𝗧𝗲𝗿𝗺𝗶𝗻𝗼𝗹𝗼𝗴𝘆 𝗦𝗲𝗿𝘃𝗲𝗿 Clinical language is complex. Sometimes fixed schemas aren’t e...
04/02/2026

𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝗔𝗰𝗰𝘂𝗿𝗮𝘁𝗲 𝗦𝗲𝗮𝗿𝗰𝗵 𝗶𝗻 𝗠𝗲𝗱𝗶𝗰𝗮𝗹 𝗧𝗲𝗿𝗺𝗶𝗻𝗼𝗹𝗼𝗴𝘆 𝗦𝗲𝗿𝘃𝗲𝗿
Clinical language is complex. Sometimes fixed schemas aren’t enough.
Accurate Search is built for teams that need flexibility and customization in medical entity extraction.

Using a pretrained clinical zero-shot entity recognition model, Accurate Search allows you to:

✔ Define or modify entity labels at runtime
✔ Map extracted entities to different code systems
✔ Customize extraction logic to fit your workflow
✔ Handle nuanced or evolving clinical terminology

This makes it ideal for:

• Research environments
• Advanced analytics
• Custom extraction schemas
• Organizations adapting to new clinical domains


Accurate Search provides probabilistic, flexible interpretation while still resolving concepts to standardized terminologies.
If you need adaptable, workflow-specific document intelligence - this is the mode to explore.
Reach out if you'd like a demo or technical walkthrough.

Get John Snow Labs Terminology Server today:

AWS: https://hubs.li/Q049rdmC0

Azure: https://hubs.li/Q049rnVj0

On Prem: https://hubs.li/Q049rsXJ0

Care coordination often fails at transition points. The Patient Journey Intelligence Platform helps identify: • Gaps bet...
04/01/2026

Care coordination often fails at transition points.

The Patient Journey Intelligence Platform helps identify:
• Gaps between referrals and specialist visits
• Delays in diagnostic follow-up
• Missed screenings
• Recurrent emergency utilization patterns

With structured journey analytics, organizations can intervene earlier — improving continuity and patient outcomes.
Better coordination starts with better visibility.

Explore Patient Journey Intelligence:
https://hubs.li/Q049hCBc0

When clinical documentation and billing codes don’t align, denials happen. Patients lose trust. Hospitals lose revenue.W...
03/31/2026

When clinical documentation and billing codes don’t align, denials happen. Patients lose trust. Hospitals lose revenue.
We’re using Generative AI to catch errors before they impact care, by linking unstructured notes to correct, compliant codes in real-time.
📉 Prevent denials
📈 Improve claim accuracy
🛡️ Reduce friction in the revenue cycle

Learn how AI can stop the cycle:
🔗 https://hubs.li/Q048H-9l0

AI in healthcare should drive measurable improvement, not just dashboards.The Patient Journey Intelligence Platform enab...
03/30/2026

AI in healthcare should drive measurable improvement, not just dashboards.
The Patient Journey Intelligence Platform enables leadership teams to:
• Visualize patient flow across service lines
• Identify systemic inefficiencies
• Support data-driven decision-making
• Align analytics with strategic care initiatives

By unifying clinical, operational, and narrative data, PJI delivers insight into how systems perform — not just how they report.

Learn more:
https://hubs.li/Q048H_720

Black-box summarization can miss the mark.Our solution provides traceable links to source content, clinical concept tagg...
03/29/2026

Black-box summarization can miss the mark.
Our solution provides traceable links to source content, clinical concept tagging, and section-aware summarization—so clinicians can trust the AI and verify key details.
Transparency isn't optional in healthcare.

Explore now: https://hubs.li/Q048J2130

Generative AI in healthcare requires domain control.The Applied Healthcare AI Summit will examine:• Private LLM deployme...
03/28/2026

Generative AI in healthcare requires domain control.

The Applied Healthcare AI Summit will examine:
• Private LLM deployment
• Safe use of generative AI in clinical settings
• RAG architectures for medical knowledge
• Guardrails and human-in-the-loop validation

Healthcare AI must be both innovative and accountable.

If your organization is exploring generative AI beyond proof of concept, this event offers grounded guidance.

Reserve your seat:
https://hubs.li/Q048J8H80

Quick registration via LinkedIn: https://hubs.li/Q048J2NH0

How reliable is terminology mapping in your AI pipeline?The Medical Terminology Server enables:• Real-time SNOMED CT, IC...
03/27/2026

How reliable is terminology mapping in your AI pipeline?

The Medical Terminology Server enables:
• Real-time SNOMED CT, ICD-10, LOINC, RxNorm mapping
• FHIR-native integration
• Version control and terminology governance
• Deployment in secure, private environments

Terminology alignment is foundational to interoperability and analytics accuracy.

See how terminology services strengthen healthcare AI systems:
https://hubs.li/Q048FYMj0

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Our Story

We believe data science, analytics & software innovators in healthcare are heroes. Our role is to support their vital work with the best quality and most valuable data. If you’re doing large amounts of data science in healthcare, we can help! Software and analytics projects depend on many datasets. Making data ready for analysis takes a lot of time and effort – 50-80% of data scientists’ time, by recent accounts. Datasets are updated on different schedules, so taking care of updates is a hassle. They have different owners, so compliance with multiple license types is an ongoing burden. On top of that, public and proprietary datasets are spread across many catalogs, not all online, so finding the right dataset is a challenge by itself. We give you turnkey data for analysis. Save months to build a clean, useable data library. Save time and improve accuracy by always having all data up to date. Call on our experts to find, acquire or generate hard-to-find datasets. Receive data in the format that tested & optimized for your big data, data science or visualization platform. Reduce duplicate internal effort by providing your team with a shared data library. Enjoy compliance piece of mind, since that’s part of the included support package.

https://www.johnsnowlabs.com/dataops-blog