The titular question was quite relevant and often discussed some thirty years ago. The thought went: “Sure, there are industries where computers are the norm, but in my industry we got just fine so far, the benefits are questionable, problems innumerable and unsolved. Can we continue as before or should we embrace this new technology?”

Today, everyone asks the same question but about Machine Learning and Artificial Intelligence. The doubts are the same – lack of expertise, lack of known path, perceived irrelevancy to the industry.

Yet, as before, the correct, even the only possible answer is a resounding yes. Read on to find out why.

What if you could speak in a chat to check what happens in Interoperability, is there any errors, and even solve some types of issues.

With MCP server, you can connect any of your MCP Client, for instance Claude, to IRIS, and ask to check on Interoperability

Continuing with the series of articles on voice file management, we are going to see how we can convert text into audio and receive the file with the chosen voice.
We will also explore how a service from OpenAI can help us analyze a text and determine the mood expressed in it.
Let's analyze how you can create your own voice file and how it can “read” your feelings.

* In-Platform Agent-Based Simulation of a Connected Factory Cluster

Author: Sergey Lukyanchikov, InterSystems

Creating a Chatbot with IRIS and Python

In this article I'm going to show how to integrate the InterSystems IRIS Database with Python to serve a Machine
Learning Model of Natural Language Processing (NLP).

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.

Audience

Those curious in exploring new GenerativeAI usecases.

Developers and analysts looking for a quick way to tame the Pattern Match operator.

In both ObjectScript and SQL this has a quite visually dense format.

Kidney Disease can be discovered from some parameters well known to the medical community. In this way, in order to help the medical community and computerized systems, especially AI, the scientist Akshay Singh published a very useful dataset for training ML algorithms in the detection/prediction of kidney disease. This publication can be found on the largest and best known data repository for ML, Kaggle at https://www.kaggle.com/datasets/akshayksingh/kidney-disease-dataset.

Audience

Those curious in exploring new GenerativeAI usecases.

Shares thoughts and rationale when training generative AI for pattern matching.

Maternal Risk can be measured from some parameters well known to the medical community. In this way, in order to help the medical community and computerized systems, especially AI, the scientist Yasir Hussein Shakir published a very useful dataset for training ML algorithms in the detection/prediction of Maternal Risk.

INTRO

Thank you community for translating an earlier article into Portuguese.
Am returning the favor with a new release of Pattern Match Workbench demo app.

Added support for Portuguese.

The labels, buttons, feedback messages and help-text for user interface are updated.

Pattern Descriptions can be requested for the new language.

Learning LLM Magic

The world of Generative AI has been pretty inescapable for a while, commercial models running on paid Cloud instances are everywhere. With your data stored securely on-prem in IRIS, it might seem daunting to start getting the benefit of experimentation with Large Language Models without having to navigate a minefield of Governance and rapidly evolving API documentation. If only there was a way to bring an LLM to IRIS, preferably in a very small code footprint....

Hello everyone, this is with great pleasure that I announce the V2 of my application 'Contest-FHIR'.

In this new version, I used new tools and techniques I discovered at the EUROPEAN HEALTHCARE HACKATHON in which I was invited by InterSystems as a guest and as a mentor to display the multiple projects I did in my intership back in April 2022.

Today I present to you the V2 of my application, it can now transform CSV to FHIR to SQL to JUPYTER notebook.