#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.

With the rapid adoption of telemedicine, remote consultations, and digital dictation, healthcare professionals are communicating more through voice than ever before. Patients engaging in virtual conversations generate vast amounts of unstructured audio data, so how can clinicians or administrators search and extract information from hours of voice recordings?

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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!

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Hey Community,

Enjoy the new video on InterSystems Developers YouTube:

⏯ The Road to AI in Healthcare @ Ready 2025

https://www.youtube.com/embed/Yqhq2JEWeCo
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Hey Community,

The InterSystems team put on our monthly Developer Meetup with a triumphant return to CIC's Venture Café, the crowd including both new and familiar faces. Despite the shakeup in both location and topic, we had a full house of folks ready to listen, learn, and have discussions about health tech innovation!

https://www.youtube.com/embed/-ER8dW6ZtQw
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This anthropic article made me think of several InterSystems presentations and articles on the topic of data quality for AI applications. InterSystems is right that data quality is crucial for AI, but I imagined there would be room for small errors, but this study suggests otherwise. That small errors can lead to big hallucinations. What do you think of this? And how can InterSystems technology help?

https://www.anthropic.com/research/small-samples-poison

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