#Artificial Intelligence (AI)

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Artificial Intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions) and self-correction.

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Article André Dienes Friedrich · 16 hr ago 13m read

CardioIris and AI-assisted development on InterSystems IRIS

An engineering walkthrough of the IRIS-CardioFlow project architecture with real code for its AI, FHIR, and connectivity layers and the role of iris-agentic-dev in a modern ObjectScript workflow.

Introduction

Monitoring cardiovascular surgical flow in real time is a classic healthcare integration problem: data arrives from heterogeneous sources, must be persisted with clinical semantics, exposed through an API, and presented in a way the care team can act on. The CardioIris repository (internally named IRIS-CardioFlow) is a lean demonstration of that scenario, built on InterSystems IRIS 2026.

#InterSystems IRIS for Health #Artificial Intelligence (AI) #Contest
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Article Pietro Di Leo · Oct 9, 2025 6m read

Enhancing FHIR Data Exploration with Local LLMs: Integrating IRIS and Ollama

Introduction

In my previous article, I introduced the FHIR Data Explorer, a proof-of-concept application that connects InterSystems IRIS, Python, and Ollama to enable semantic search and visualization over healthcare data in FHIR format, a project currently participating in the InterSystems External Language Contest.

In this follow-up, we’ll see how I integrated Ollama for generating patient history summaries directly from structured FHIR data stored in IRIS, using lightweight local language models (LLMs) such as Llama 3.2:1B or Gemma 2:2B.

The goal was to build a completely local AI pipeline that can extract, format, and narrate patient histories while keeping data private and under full control.

All patient data used in this demo comes from FHIR bundles, which were parsed and loaded into IRIS via the IRIStool module. This approach makes it straightforward to query, transform, and vectorize healthcare data using familiar pandas operations in Python. If you’re curious about how I built this integration, check out my previous article Building a FHIR Vector Repository with InterSystems IRIS and Python through the IRIStool module.

Both IRIStool and FHIR Data Explorer are available on the InterSystems Open Exchange — and part of my contest submissions. If you find them useful, please consider voting for them!

#InterSystems IRIS #Artificial Intelligence (AI) #FHIR #Large Language Model (LLM) #Prompt Engineering #Python
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Announcement Evgeny Shvarov · May 28

Technology Bonuses for the InterSystems Programming Contest: AI Agents for FHIR

Hi Developers!

Here are the technology bonuses for the InterSystems Programming Contest: AI Agents for FHIR that will give you extra points in the voting:

  • Implement suggested task - 5
  • InterSystems FHIR Server usage - 2
  • Vector Search usage - 4
  • Embedded Python - 3
  • LLM AI or LangChain usage: Chat GPT, Bard, and others - 3
  • Docker container usage - 2 
  • ZPM Package deployment - 2
  • Online Demo - 2
  • Implement InterSystems Community Idea - 4
  • Find a bug in Vector Search or Embedded Python - 2
  • First Article on Developer Community - 2
  • Second Article On DC - 1
  • First Time Contribution - 3
  • Video on YouTube - 3
#IRIS contest #InterSystems IRIS for Health #Artificial Intelligence (AI) #Contest #FHIR #Interoperability
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Article Gabriel Ing · May 27 8m read

An Introduction to AI Hub, Part 2: Custom MCP Servers

Welcome back to a series of introductory articles on AI Hub, the new product feature currently in an early access program! (links: EAP Site for download, documentation)

In the last article, we covered how to create agents and agent tools directly in ObjectScript using the new %AI classes. However, sometimes, instead of creating a new agent, you just want to add some custom tools to an existing agent so you can ask your local claude code, codex, copilot or other agent of choice to query your data directly. This is where MCP Servers might come in.

#InterSystems IRIS #InterSystems IRIS for Health #Artificial Intelligence (AI) #Early Access Program (EAP) #Generative AI (GenAI)
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Announcement Zelong Wang · May 7

[Demo Video] Health Galaxy: AI-Enabling Healthcare Applications

#North American Demo Showcase entry. 

>> Answer the question below to be entered in the raffle!

⏯️ Health Galaxy: AI-Enabling Healthcare Applications

Health Galaxy creates an AI access point on top of any FHIR server, bringing healthcare into the AI future that has become a reality for many other industries. 

  • AI access: Health Galaxy gives AI agents a single gateway into any healthcare system, so they can pull patient data, schedule appointments, and check insurance automatically instead of a human doing it manually. 
  • Ease of use: You point it at an existing FHIR endpoint, click a button, and it generates an MCP endpoint automatically from the capability statement. 
  • FHIR: Since we are using FHIR, we can leverage both the storage and exchange capabilities of InterSystems IRIS.

🗣 Presenter: @Zelong Wang, Sales Engineer at InterSystems 

#IRIS contest #InterSystems IRIS for Health #Artificial Intelligence (AI) #FHIR #Video
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Announcement Vic Sun · May 5

[Demo Video] Triage Chatbot

#North American Demo Showcase entry. 

>> Answer the question below to be entered in the raffle!

⏯️ Triage Chatbot

We are using IRIS for Health to develop an agentic AI chatbot workflow that can interact with a patient using voice commands, reach out to an EHR or other system for context, and provide recommendations back.

Presenters: 
🗣 @Vic Sun, Sales Engineer at InterSystems 
🗣 @Brad Nissenbaum, Sales Engineer at InterSystems 
🗣 Danielle Micciantuono, Clinical Solutions Specialist at InterSystems

#IRIS contest #InterSystems IRIS for Health #Artificial Intelligence (AI) #Video
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Announcement Simon Sha · Apr 16

[Demo Video] AI Assistants for the Unified Care Record Powered by Gemini

#North American Demo Showcase entry. 

>> Answer the question below to be entered in the raffle!

⏯️ AI Assistants for the Unified Care Record Powered by Gemini

In this demo, you will see how Gemini works directly with FHIR data, and how it leverages the harmonized dataset provided by InterSystems Unified Care Record. It also showcases multiple AI assistants helping multiple groups of users, e.g. clinicians, patients.

🗣 Presenter: @Simon Sha, Sales Architect at InterSystems

#InterSystems IRIS for Health #IRIS contest #Artificial Intelligence (AI) #FHIR #Video
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Article Yu Han Eng · Oct 5, 2025 3m read

IRIS Audio Query - Development

IRIS Audio Query is a full-stack application that transforms audio into a searchable knowledge base.

Project Structure

community/
├── app/                   # FastAPI backend application
├── baml_client/           # Generated BAML client code
├── baml_src/              # BAML configuration files
├── interop/               # IRIS interoperability components
├── iris/                  # IRIS class definitions
├── models/                # Data models and schemas
├── twelvelabs_client/     # TwelveLabs API client
├── ui/                    # React frontend application
├── main.py                # FastAPI application entry point
└── settings.py            # IRIS interoperability entry point
#InterSystems IRIS #Artificial Intelligence (AI) #Embedded Python #Vector Search
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Article Pietro Di Leo · May 6 18m read

Model Context Procotol (MCP) with InterSystems IRIS - From Zero to Hero

Introduction

Today, coding assistants like Claude, GitHub Copilot and Cursor have transformed the way developers write code. However, these tools are limited by being isolated from the systems and data sources that developers work with daily. This limitation can be overcome through the Model Context Protocol (MCP), an open standard designed to connect AI assistants to external data sources and tools in a secure and standardized way.

In this review article, we'll explore the current state-of-the-art regarding the MCP within the InterSystems ecosystem.

#InterSystems IRIS #Artificial Intelligence (AI) #Generative AI (GenAI) #Tools #VSCode
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Announcement Jesse Reffsin · Apr 21

[Demo Video] AI-Assisted Rare and Complex Disease Detection

#North American Demo Showcase entry. 

>> Answer the question below to be entered in the raffle!

⏯️ AI-Assisted Rare and Complex Disease Detection

This demo shows how InterSystems Health Gateway can be used to pull in outside patient records from networks like Carequality, CommonWell, and eHealth Exchange, creating a more complete longitudinal view in a clinical viewer. That full record is then analyzed by AI to surface potential rare disease considerations with clear reasoning, helping clinicians see patterns they might otherwise miss.

Presenters: 
🗣 @Jesse Reffsin, Senior Sales Engineer at InterSystems
🗣 @Georgia Gans, Sales Engineer at InterSystems
🗣 @Annie Tong, Sales Engineer at InterSystems

#InterSystems IRIS for Health #IRIS contest #Artificial Intelligence (AI) #Video
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Article Clinovera · May 9 10m read

Proven AI — Modernizing Legacy Systems With AI Coding Agents

Many organizations that operate systems built on legacy technology stacks are facing significant support and maintenance complexities. They are eager to modernize, but the transition is usually prohibitively complex and expensive. These challenges apply to virtually any legacy tech, while InterSystems-based systems have their own unique nuances.

Key modernization challenges include:

  • Refactoring massive amounts of code, including identifying and removing obsolete "dead code."
  • Managing complex business logic accumulated organically over decades.
#Caché #InterSystems IRIS #Artificial Intelligence (AI) #CSP #Contest #Docker #ObjectScript #Open Source
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Article Gabriel Ing · May 7 4m read

An Introduction to AI Hub, Part 1: Agents in ObjectScript

For those of you that weren't at READY last week, you may have missed the exciting announcement that the Early Access Program for AI Hub is officially open. It was announced during an amazing demo from @Benjamin De Boe and @Jeff Fried, I recommend catching up with this demo when the recording is released!  I had the opportunity to play with AI Hub in advance, and thought I might share an introduction with the community.

#InterSystems IRIS #InterSystems IRIS for Health #Documentation #Other #Artificial Intelligence (AI) #Early Access Program (EAP) #Generative AI (GenAI) #ObjectScript
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Article Jorge Jaramillo Herrera · May 5 19m read

Explainability in ML Models

This article introduces SHAP explainability methods as an approach to understand the reasons behind predictions in machine learning black-box models. It also includes a simple Jupyter notebook that you can use and modify to gain hands-on experience with these concepts:

https://www.kaggle.com/code/jorgeivnjh/explainability-in-ml-models

https://github.com/JorgeIvanJH/Explainability-in-ML-models

We will leverage these concepts for a future implementation in our Continuous Training Pipeline: https://community.intersystems.com/post/complementing-iris-mlflow-continuous-training-ct-pipeline

#Other #Artificial Intelligence (AI) #Machine Learning (ML) #Python
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Article Suprateem Banerjee · Apr 30 14m read

IRIS Agents: Building Agents on IRIS!

Image 

Ever since I started using IRIS, I have wondered if we could create agents on IRIS. It seemed obvious: we have an Interoperability GUI that can trace messages, we have an underlying object database that can store SQL, Vectors and even Base64 images. We currently have a Python SDK that allows us to interface with the platform using Python, but not particularly optimized for developing agentic workflows. This was my attempt to create a Python SDK that can leverage several parts of IRIS to support development of agentic systems.

#InterSystems IRIS #Artificial Intelligence (AI) #Business Process (BPL) #Generative AI (GenAI) #Large Language Model (LLM) #ObjectScript #Python #SQL
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Article Zhong Li · Apr 22 12m read

The 3rd Generation of Agents: How "Harness Engineering" Changed Games Again

In last post I talked about iris-copilot, an apparent vision that in near future any human language is a programming language for any machines, systems or products. Its agent runners were actually using such so-called 3rd generation of agents. I want to keep/share a detailed note on what it is, for my own convenience as well. It was mentioned a lot times in recent conversations that I was in, so probably worth a note.

#InterSystems IRIS #Documentation #Artificial Intelligence (AI) #Generative AI (GenAI)
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Question Mark Charlton · Apr 13

Claude Code vs ISFS. Has anyone cracked how to get one to see the other?

I'm starting to play more with AI enabled coding. 
I've been using Github Copilot inside Visual studio code, which is very good at coming up with autocomplete suggestions that are accurate and useful. (Along with some utter rubbish, naturally).
For web development I'm starting to use Claude Code in VS Code to help create web sites and integrations. I want to see how it can help with IRIS development. 

However I can't get claude to read any iris code directly as I'm connected to my server via isfs server connections.

#InterSystems IRIS #Artificial Intelligence (AI) #Development Environment #Generative AI (GenAI)
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Article Henry Pereira · Apr 2, 2025 17m read

Command the Crew

Image generated by OpenAI DALL·E

I'm a huge sci-fi fan, but while I'm fully onboard the Star Wars train (apologies to my fellow Trekkies!), but I've always appreciated the classic episodes of Star Trek from my childhood. The diverse crew of the USS Enterprise, each masterminding their unique roles, is a perfect metaphor for understanding AI agents and their power in projects like Facilis. So, let's embark on an intergalactic mission, leveraging AI as our ship's crew and  boldly go where no man has gone before

#InterSystems IRIS #Open Exchange #API #Analyzer #Artificial Intelligence (AI) #Best Practices #Embedded Python #Interoperability #Python #REST API
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Article Alberto Fuentes · Sep 1, 2025 6m read

Build a Customer Support AI Agent with smolagents + InterSystems IRIS (SQL, RAG & Interoperability)

Customer support questions span structured data (orders, products 🗃️), unstructured knowledge (docs/FAQs 📚), and live systems (shipping updates 🚚). In this post we’ll ship a compact AI agent that handles all three—using:

  • 🧠 Python + smolagents to orchestrate the agent’s “brain”
  • 🧰 InterSystems IRIS for SQL, Vector Search (RAG), and Interoperability (a mock shipping status API)

⚡ TL;DR (snack-sized)

  • Build a working AI Customer Support Agent with Python + smolagents orchestrating tools on InterSystems IRIS (SQL, Vector Search/RAG, Interoperability for a mock shipping API).
#InterSystems IRIS #Artificial Intelligence (AI) #Interoperability #Large Language Model (LLM) #Python #SQL #Tools #Vector Search
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Article Roy Leonov · Apr 14 7m read

Teaching an AI Coding Agent to Work Safely with a Legacy InterSystems Caché Application

We didn't start with a big AI strategy.

We had a legacy InterSystems Caché 2018 application, a lot of old business logic, and a practical need: build a new UI and improve code that had been running for years. At first, I thought an AI coding agent would help only with a small part of the work. Maybe some boilerplate, some REST work around the system, and a bit of help reading old ObjectScript.

In practice, it became much more than that.

#Caché #API #AWS #Artificial Intelligence (AI) #Cloud #Development Environment #ObjectScript #Terminal
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Announcement John Murray · Apr 14

Introducing gj :: servAI - a VS Code extension to help you use MCP servers targeting IRIS

Over the past year there have been a few DC articles offering MCP servers designed to connect to IRIS and help the AI features of VS Code and its cousins do a better job. For example:

#InterSystems IRIS #Artificial Intelligence (AI) #Development Environment #Generative AI (GenAI) #George James Software #Large Language Model (LLM) #VSCode
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Article Jinyao · Apr 2 2m read

No-Hallucinate MCP: Real-time Routine Context for ObjectScript

Motivation

Why do we need this?

  1. Lack of Compiled Context: AI tools only see source code; they don't know what the final compiled routine looks like.

  2. Macro Hallucination: Because AI doesn't see our #include files or system macros, it often makes them up, wasting time during debugging.

  3. The Documentation Gap: Deep logic optimization often requires understanding internal macros that aren't fully covered in public documentation.

  4. Manual Overhead: Currently, the only way to fix this is to manually use the IRIS VS Code extension to find the "truth" in the routine.

#Caché #InterSystems IRIS #Open Exchange #Artificial Intelligence (AI) #Generative AI (GenAI)
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Article Jorge Jaramillo Herrera · Mar 30 7m read

Complementing IRIS with MLflow for a Continuous Training (CT) pipeline

A Continuous Training (CT) pipeline formalises a Machine Learning (ML) model developed through data science experimentation, using the data available at a given point in time. It prepares the model for deployment while enabling autonomous updates as new data becomes available, along with robust performance monitoring, logging, and model registry capabilities for auditing purposes.

InterSystems IRIS already provides nearly all the components required to support such a pipeline. However, one key element is missing: a standardised tool for model registry.

#InterSystems IRIS #InterSystems IRIS for Health #Artificial Intelligence (AI) #Docker #IntegratedML #Machine Learning (ML)
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