On science, detours, and the slow realization that medicine was the destination all along.

It started as a small thing. Just a thought, really, planted by my dad in a conversation I was probably too young to fully understand. He talked about what it might mean to become a doctor - the kind of person who gets to sit with someone on the worst day of their life and actually do something about it.

I did not know yet that the idea had lodged itself somewhere quiet in my mind. But it had. And it stayed there for years, patient, waiting for me to catch up to it.

As a kid I was hungry for science, and not in any tidy, sensible way. I wanted all of it at once.

Biology pulled me in first - the sheer improbability of living things, a body as billions of small negotiations happening at once and somehow holding together. But biology was never enough on its own. I wanted to know how the stars worked, so I fell into astronomy and the strange comfort of realizing how large everything is and how small and lucky we are inside it. Then physics, because I wanted the rules underneath the rules: why things fall, orbit, hold, and break.

And animals. Always animals. There is a particular kind of person who cannot walk past a creature without wanting to understand it, and I was that kid - endlessly curious about how something so different from me could be so alive in the same way I was.

Looking back, the thread was obvious. I was never interested in facts for their own sake. I was interested in systems - how complicated things fit together and keep running. I just did not yet know what that curiosity was supposed to become.

Somewhere along the way, I got impatient.

Medicine is a long road, and I knew it. When you are young and capable and in a hurry, a long road can start to feel less like a calling and more like a delay. So I began looking for a faster path, one that could put my mind to work sooner. Data science pulled at me. So did IT, cybersecurity, and the fast-moving world of AI.

For a while I told myself this was a departure - that I was setting the doctor idea down and picking up something more practical, more immediate, more mine.

I threw myself into it. I learned to think in data, to sit with a messy pile of information and coax a real signal out of it. I learned how systems get defended and where they get broken. I learned what modern AI can actually do, and just as importantly, what it cannot. It was genuinely thrilling. I was good at it, and being good at something is its own kind of gravity.

But the thing about a detour is that you do not always realize you are still circling the place you started.

The turn came slowly, and then all at once.

The more I worked in data and security and AI, the more I kept noticing where those disciplines actually pointed. I would be deep in a dataset and realize the most interesting questions were clinical ones. I would think about how systems fail and find myself thinking about how bodies fail, how diagnoses get missed, how the right piece of information at the right moment can change an outcome. The tools I had picked up to get away from medicine kept quietly handing me back to it.

Data science is pattern recognition under uncertainty. So is a diagnosis. Cybersecurity is the discipline of protecting something fragile and vital from harm you cannot always see coming. So is caring for a patient. AI, at its best, is a way to extend human judgment, not replace it - and medicine has spent its entire history trying to do exactly that with every new tool it has ever adopted.

I had not left medicine at all. I had been standing right next to it the whole time, learning the language it was about to start speaking.

So I came back, but not empty-handed.

I did not return to medicine as the same person who left. I returned carrying a toolkit that most people walking into this field do not have. I understand data not as an abstraction but as something you can actually build with. I understand how modern systems are secured and where they are vulnerable - which matters enormously in a world where patient information and clinical decisions increasingly live inside software. And I understand what AI is genuinely capable of, which means I can be excited about it without being naive, and cautious about it without being afraid.

The longer path stopped feeling like a delay and started feeling like an advantage.

Some of that path has been about getting close to patients, not just concepts. Working as an EMT taught me what no dataset ever could: what it feels like to be the person who shows up, what it means to stay calm for someone who cannot, how much of care is presence before it is ever procedure. Data can tell you what usually happens. It cannot tell you what this particular frightened human in front of you needs right now. I want to be fluent in both.

That is the work I am doing now, in my graduate studies at Wake Forest, where I get to sit at exactly the intersection I once thought I had to choose between. I am spending time inside real emergency medical data, thinking about how the information that flows through EMS could be used to help clinicians see sooner and decide better.

The doctor I want to become is not one who chose medicine instead of technology, or technology instead of medicine. She is someone who understands that the two were never really separate - that the future of good care lives precisely where careful human judgment meets powerful new tools.

It took me a long way around to understand that. I went looking for a shortcut and found a foundation instead. And the small thought my dad planted years ago, the one I was too young to understand and too impatient to chase, turned out to be right where I left it, waiting for me to arrive with everything I had picked up on the way.