AI can feel like a black box. This plain-language guide explains how it works for clinics.
Artificial Intelligence (AI) is no longer a futuristic concept; it’s already functioning behind the scenes in some applications and reshaping how clinics operate. But for many healthcare professionals, the inner workings of AI can feel like a black box. What follows here is a breakdown of the foundational concepts behind AI in simple terms, helping you understand what’s really going on behind the scenes and why it matters for your clinic.
By Mike Checkley, President, and Brian Ellis, Senior Director of Product
AI Is a Tool, Not a Replacement
Let’s start with a key message from Brian Ellis, Senior Director of Product at Accuro:
“AI is a tool—a very powerful tool, a transformational one—but one that we just have to make sure we apply in the right way.”
AI isn’t here to replace clinicians or office staff. It’s here to support them by reducing administrative burden, improving efficiency, and helping bring the right information forward at the right time. Think of it as a digital assistant that can listen, summarize, and suggest, but that always leaves the final call to the provider.
What Powers AI in Healthcare?
Here are three foundational concepts that help explain how AI works in tools like the ones available for Accuro EMR:
- Large Language Models (LLMs)
LLMs are the brains behind many AI tools. They’ve been trained on massive amounts of text to understand and generate human-like language. When you ask an AI to summarize a patient visit or draft a referral letter, it’s using an LLM to do the heavy lifting.
But LLMs don’t “know” things the way humans do. They generate responses based on patterns present in the data they’re fed. That’s why context, and how you apply the tool, is everything.
- Session Memory
Session memory is basically short-term memory for AI. It remembers what’s happening during your current interaction, but it forgets it afterward unless explicitly designed to retain it.
“It’s just giving the context, getting an output, and forgetting it. The data’s not used for anything after the fact.” —Brian Ellis
This is important for privacy. Most clinical AI tools don’t store your data or use it to train future models. They process it temporarily, generate the output, and move on.
- Vector Embeddings
It may sound technical, but vector embeddings are simply the way AI understands meaning. Instead of just matching keywords, AI converts concepts into numbers and maps them in a kind of “idea space.” That’s how it knows that “cold” and “flu” are related, even if the exact words don’t match.
Bringing it all together
For a sense of how these pieces interact in practice, imagine this scenario: You dictate your patient visit into an AI tool. The LLM interprets your words and drafts a structured note. Session memory keeps track of everything you’ve said during that visit so the note stays coherent, but it forgets it afterward to maintain privacy. And vector embeddings help the AI link your mention of “strep throat” to relevant guidelines and related terms, so nothing important is missed.
It’s these three concepts working together that turn a simple voice note into a clean, actionable clinical document.
Why This Matters for Clinics
Understanding these concepts helps clinics make informed decisions about AI tools. It also builds trust, because when you know how something works, you’re more likely to use it confidently.
Mike Checkley, President of Accuro EMR, puts it this way:
“Our job is to make AI real, and we do that by showing clinics how it helps with their everyday tasks: saving time, reducing typing, and helping clinicians focus on care.”
By Mike Checkley, President, and Brian Ellis, Senior Director of Product