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#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.

Learn more.

Hey Community,

It's time for the first programming contest of the year, and there's a surprise so read on! Please welcome:

🏆 InterSystems AI Programming Contest: Vector Search, GenAI, and AI Agents 🏆

Duration: March 17 - April 6, 2025

Prize pool: $12,000 + a chance to be invited to the Global Summit 2025!

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Hi Community,

Traditional keyword-based search struggles with nuanced, domain-specific queries. Vector search, however, leverages semantic understanding, enabling AI agents to retrieve and generate responses based on context—not just keywords.

This article provides a step-by-step guide to creating an Agentic AI RAG (Retrieval-Augmented Generation) application.

Implementation Steps:

  1. Create Agent Tools
    • Add Ingest functionality: Automatically ingests and index documents (e.g., InterSystems IRIS 2025.1 Release Notes).
    • Implement Vector Search Functionality
  2. Create Vector Search Agent
  3. Handoff to Triage (Main Agent)
  4. Run The Agent
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Article
· Mar 17, 2025 2m read
Ollama AI with IRIS

In this article I will be discussing the use of an alternative LLM for generative IA. OpenIA is commonly used, in this article I will show you how to use it and the advantages of using Ollama

In the generative AI usage model that we are used to, we have the following flow:

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Hi developers!

This will be a very short article as in April 2025 with Lovable and other Prompt-to-UI tools it becomes possible to build the frontend with prompting. Even to the folks like me who is not familiar with modern UI techics at all.

Well, I know at least the words javascript, typescript and ReactJS, so in this very short article we will be building the ReactJS UI to InterSystems FHIR server with Lovable.ai.

Let's go!

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Hi Community!

We’re excited to announce that several winners of the InterSystems AI Programming Contest have been invited to showcase their projects at the Tech Exchange during InterSystems Ready 2025!

Join us on Wednesday, June 25, to explore innovative, real-world solutions built with InterSystems IRIS, AI, LLMs, and intelligent agent technologies — directly from the developers who created them:

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Article
· Apr 1, 2025 1m read
IRIS-Intelligent Butler

# IRIS-Intelligent Butler
IRIS Intelligent Butler is an AI intelligent butler system built on the InterSystems IRIS data platform, aimed at providing users with comprehensive intelligent life and work assistance through data intelligence, automated decision-making, and natural interaction.
## Application scenarios
adding services, initializing configurations, etc. are currently being enriched
## Intelligent Butler

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I just realized I never finished this serie of articles!

GIF de Shame On You Meme | Tenor

In today's article, we'll take a look at the production process that extracts the ICD-10 diagnoses most similar to our text, so we can select the most appropriate option from our frontend.

Looking for diagnostic similarities:

From the screen that shows the diagnostic requests received in HL7 in our application, we can search for the ICD-10 diagnoses closest to the text entered by the professional.

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☤ Care 🩺 Compass 🧭 - Proof-of-Concept - Demo Games Contest Entry

Introducing Care Compass: AI-Powered Case Prioritization for Human Services

In today’s healthcare and social services landscape, caseworkers face overwhelming challenges. High caseloads, fragmented systems, and disconnected data often lead to missed opportunities to intervene early and effectively. This results in worker burnout and preventable emergency room visits, which are both costly and avoidable.

Care Compass was created to change that.

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Introducing Smart Clinical Sidechick — the intelligent, no-drama partner your EHR wishes it could be. She reads FHIR data in real time, interprets lab results without ghosting, and explains clinical alerts like she actually cares. Built with GPT-4 brains and YAML sass, she’s not here to replace your main EHR—just to make it look bad. Tired of irrelevant alerts and cryptic warnings? Sidechick serves up real, explainable insights, not vague “elevated risk” vibes. And when your backend crashes, she doesn’t panic—she self-heals.

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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)

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Hey Community!

We're happy to share the next video in the series dedicated to Gen AI on our InterSystems Developers YouTube:

⏯ Energy Consumption for AI

https://www.youtube.com/embed/o99hGFoCeuU
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Hi Developers!

Here're the technology bonuses for the InterSystems AI Programming Contest: Vector Search, GenAI and AI Agents that will give you extra points in the voting:

  • Agent AI solution - 5
  • Vector Search usage - 4
  • Embedded Python - 3
  • LLM AI or LangChain usage: Chat GPT, Bard, and others - 3
  • IntegratedML usage - 3
  • Docker container usage - 2
  • ZPM Package deployment - 2
  • Online Demo - 2
  • Implement InterSystems Community Idea - 4
  • Find a bug in Vector Search, or Integrated ML, or Embedded Python - 2
  • First Article on Developer Community - 2
  • Second Article On DC - 1
  • First Time Contribution - 3
  • Video on YouTube - 3
  • Suggest a new idea - 1

See the details below.<--break->

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Hi Community,

We're excited to share the new video in the "Rarified Air" series on our InterSystems Developers YouTube:

⏯ Leading with Empathy: The Human Side of Customer Centricity

https://www.youtube.com/embed/qIb43czL9LY
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In the previous article, we saw how to build a customer service AI agent with smolagents and InterSystems IRIS, combining SQL, RAG with vector search, and interoperability.

In that case, we used cloud models (OpenAI) for the LLM and embeddings.

This time, we’ll take it one step further: running the same agent, but with local models thanks to Ollama.

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I am brand new to using AI. I downloaded some medical visit progress notes from my Patient Portal. I extracted text from PDF files. I found a YouTube video that showed how to extract metadata using an OpenAI query / prompt such as this one:

ollama-ai-iris/data/prompts/medical_progress_notes_prompt.txt at main · oliverwilms/ollama-ai-iris

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Hi Community,

We're continuing to improve and teach our Developer Community AI, and in this iteration, we've added descriptions of Open Exchange Applications to the knowledge base!

This new feature will allow DC AI to search app descriptions to answer your questions. This will give you better answers that require programming, not just general information.

To add this knowledge to your search, just tick the checkbox Open Exchange Applications:

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#InterSystems Demo Games entry

⏯️ Copilot for InterSystems Embedded BI

The Co-Pilot enables you to leverage InterSystems BI without deep knowledge in InterSystems BI. You can create new cube, modify existing cubes or leverage existing cubes to plot charts and pivots just by speaking to the copilot.

Presenters:
🗣 @Michael Braam, Sales Engineer Manager, InterSystems
🗣 @Andreas Schuetz, Sales Engineer, InterSystems
🗣 @Shubham Sumalya, Sales Engineer, InterSystems

https://www.youtube.com/embed/LSieoGzLFE4
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#InterSystems Demo Games entry

⏯️ Healthcare AI Agent Platform

An AI Agent Platform specifically built for the healthcare industry that requires no technical background to utilize. By simply defining a goal or initiative, a swarm of AI agents will conduct operations to achieve the stated end goal and measure their own efficacy along the way to continuously improve. Healthcare organizations today are facing a myriad of challenges that impact financial and clinical performance. These problems are well known, and there is a general consensus on how many of them can be alleviated with AI. However, healthcare organizations face unique challenges in implementing AI:

  • HIPAA/Regulatory compliance - Data quality & accessibility - Embedding AI into current clinical workflows, i.e. integrating AI in such a way that it does not require clinicians to re-orient their processes. It should complement existing workflows, not disrupt them.
  • Implementation cost/high technical barrier of entry – staff needed to build and maintain AI processes can become expensive
  • Ambiguous ROI calculation – careful considerations must be made to properly measure and understand the efficacy of AI integrations. InterSystems is uniquely positioned to address these challenges, enabling healthcare organizations to implement AI with minimal burden.

Presenters:
🗣 @Daniel Cole, Sales Engineer, InterSystems
🗣 @Jeff Morgan, Sales Engineer, InterSystems
🗣 @Raef Youssef, Sales Engineer, InterSystems
🗣 @Jose Ruperez, Senior Sales Engineer, InterSystems
🗣 @Harry Tong, Principal Solutions Architect, InterSystems
🗣 @Nicholai Mitchko, Sales Engineering Manager, InterSystems

https://www.youtube.com/embed/VC3DmmCK6Pw
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#InterSystems Demo Games entry

⏯️ Closing the Scientific Knowledge Gap with AI

For venture capitalists (VCs), evaluating research can be challenging. While researchers typically possess years of training and deep expertise in their field, the VCs tasked with assessing their work often lack domain-specific knowledge. This can lead to incomplete understanding of scientific data and an inability to direct organizational initiatives. To solve this problem, we have designed a solution that empowers VCs with AI-driven due diligence: ResearchExplorer. ResearchExplorer is powered by InterSystems IRIS and GPT-4o to help analyze private biomedical research alongside public sources like PubMed using Retrieval-Augmented Generation (RAG). Users submit natural language queries, and the system returns structured insights, head-to-head research comparisons, and AI-generated summaries. This allows users to bridge expertise gaps while securely protecting proprietary data.

Presenters:
🗣 @Jesse Reffsin, Senior Sales Engineer, InterSystems
🗣 @Lynn Wu, Sales Engineer, InterSystems

https://www.youtube.com/embed/zsV3LhGDP1Q
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