By Laura, BSN, RN, CRNI | ThisRN
I heard about AI on public radio.
I don’t remember exactly when, but I remember the feeling — that immediate, electric sense that something significant had just entered the world and I needed to understand it. I’ve always been that way. When something captures my curiosity I don’t dabble. I dive.
So I dove.
From public radio to the deep end
Within a short time of that first radio segment I was using AI tools directly — there were no courses yet, no guides, no prompt engineering tutorials. It was just me and a chat interface and a lot of curiosity. I learned by doing, by asking, by pushing to see what was possible. Eventually courses caught up — I went on to take several on Coursera covering AI fundamentals and prompt engineering — but by then I had already been in the deep end for a while.
I got my whole family using AI. We would share discoveries with each other, amazed at what was possible, like a group of people who had all stumbled onto the same extraordinary secret at the same time.
Then my brother gave me a tip that changed everything.
My brother has been visually impaired his whole life and is now fully blind — but that has never slowed him down. He became a lawyer. He was an early AI adopter, a paid subscriber to AI tools long before most people knew what a large language model was. Right now he is using AI to help write a novel, to make his legal work more manageable, and to explore ways to improve existing tools for the blind community. He is one of the most forward-thinking people I know, and he happens to be my brother.
In one of our many conversations about AI he pointed me toward something I hadn’t considered: building a private, local AI on my own computer. An AI that runs entirely on my machine, uses no internet connection, and belongs completely to me.
I went home and built one.
On my MacBook Air M4 I installed Ollama, LM Studio, and AnythingLLM. I pulled AI models locally. I fed them public domain texts — philosophy, wisdom traditions, books that have shaped how humans think about the mind and potential. I wrote custom system prompts to give my AI a voice that felt right to me. It took patience and tinkering and more than a few late nights, but I got it working.
I felt like a mad scientist, gleefully rubbing my hands together in anticipation. Honestly I still do.
What my industry thinks about all of this
Here’s the reality: the company I work for hasn’t implemented AI in any formal way yet. Healthcare as an industry moves slowly — for understandable reasons. Patient safety, liability, privacy regulations, clinical validation. These are not small concerns and I respect them.
But that doesn’t mean I have to wait.
The nurses and healthcare workers who will shape how AI gets used in clinical settings are not going to be the ones who waited for a memo. They’re going to be the ones who were already curious, already experimenting, already thinking hard about the possibilities and the pitfalls before anyone handed them a policy document.
I want to be one of those people.
What I actually use AI for right now
Not for patient care — healthcare isn’t quite there yet, at least not in my corner of it. Honestly, most of what I use AI for right now is personal projects — some of which touch on nursing, some of which go in entirely different directions. I have a lot of ideas in motion and AI is the engine helping me build them.
I use it for learning, for research, for thinking out loud about problems I’m trying to solve. I use it to accelerate things that used to take hours. And I use it to create — including this blog, which would have taken me far longer to launch without AI helping me think through every step.
I’m not going to pretend I’m using AI to revolutionize patient care in my daily work right now. That’s not where I am. But I am using it to build things, learn things, and move toward a version of my life and career that looks very different from where I started. That feels like enough for now.
What I think AI means for nursing
I’m no longer working in a hospital, so I won’t pretend to speak for bedside nurses navigating documentation systems and clinical decision tools in real time. That’s a conversation I’ll leave to people closer to it.
What I will say is this: the potential is enormous. Not just for efficiency or documentation — though those matter — but for knowledge. For learning. For giving nurses access to the kind of deep, synthesized clinical information that used to require years of experience or expensive continuing education to accumulate.
AI will not replace nurses. But nurses who understand AI will have capabilities that nurses who don’t simply won’t. That gap is going to matter.
I’d rather be on the right side of it.
What’s next for me
A more powerful MacBook is on the horizon — more RAM, more processing power, more room to experiment with larger and more capable models. More courses. More tinkering. More ideas becoming real things.
If you’re a nurse who is curious about AI and don’t know where to start — start anywhere. Take a free course. Ask an AI tool a clinical question and see what happens. Build something small. Follow people who are exploring this space and pay attention to what they’re learning.
And if you have someone in your life who is already ahead of the curve — listen to them. My brother, a visually impaired lawyer who was paying for AI tools before most people had heard of them, taught me something that opened up an entire world. The best teachers show up in unexpected places.
Curiosity doesn’t care where it comes from. Neither does progress.
Laura is a registered nurse with 11 years of clinical experience, including oncology care, travel nursing, and targeted drug delivery. She holds certifications as a BSN, RN, and CRNI (Certified Registered Nurse Infusion).
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