
Overview
This article is for anyone who, like me, is interested in pursuing AI research in the healthcare space. Just under a month ago, I began my Master’s program research in partnership with a local hospital. I want to give you an idea of my background, my current day-to-day work, and my initial thoughts on the field.
As with anything, what I have to say will almost certainly not be applicable to all AI healthcare research, but I hope you can walk away with some general concepts.
My Background
I am a 23 year old Master’s student at Ohio State University studying… Mechanical Engineering? And my undergraduate degree? Mechanical Engineering… so how did I get here? Let’s walk it back a bit.
Since I was maybe 10, I have loved computers. I still remember telling my dad that I wanted to learn how to make video games and then coming back from school to a crisp new copy of "C++ For Dummies" sitting on my bed. I didn’t know what an integer was, what the word variable meant, or how any of this got me closer to making the spiritual successor to Star Wars Battlefront 2, but that didn’t matter. Because after spending an hour sweating over a hot keyboard, installing some ancient version of Dev-C++, I got to hit the "Run" button, and something magical happened…

I made my computer speak to me. Somehow, all this gibberish I didn’t understand let my computer talk. From that point on, I was almost always doing something computer related, whether it was trying to mod my favorite games or making my own basic text adventures. Much of the mystery behind computers has vanished for me, but I have always been in awe of their capabilities.
This is my dream job, and it took a complete coincidence for me to realize it.
So now that I’ve thoroughly ousted myself as a hyper-nerd, you may be wondering why, if I love computers so much, I decided to study Mechanical Engineering instead. The main reason was that I feared I wouldn’t get to work on physical problems if I became a programmer. My vision would fade from staring at a computer screen all day, I would develop horrible posture, my teeth would fall out, etc. No one warned me that all of that would be the reality of mechanical engineering as well!
But realizing those realities after an extended internship at Honda wasn’t what changed my path. A strange crossroads of my capstone project being medical in nature and my hopeful Master’s program advisor looking into artificial intelligence research with hospitals near campus led me here. To put it plainly, this is my dream job, and it took a complete coincidence for me to realize it. Focusing on programming again feels like coming home.
My Day-To-Day
So now that I’ve talked about myself, what is it that I actually do? Like with most jobs, I don’t have a set list of to-dos coming from on high, but there are several things I do every day. Here’s the three most common for me, in no particular order.
- Fiddling with Data

This is true for basically any Machine Learning job, and it can’t be overstated. Most of my days have been focused on cleaning and processing my own data or reading about how other researchers with similar data have cleaned and processed theirs. How did they handle missing values? Did they have any uniquely engineered features? How did they normalize their data? Was their model able to achieve good results with their methods? These are all questions you’ll be asking when working through the papers of your fellow researchers.
- Working with Stakeholders
Obviously, the purpose of research is to achieve some result and share it with the scientific community. But what result? What if Stakeholder A has a different idea from Stakeholder B of what you should be doing? You need to make sure that everyone is on the same page, heading towards the same goal, and most importantly, you need to make sure that you understand where you’re headed. While your stakeholders are nominally in charge, you are likely the subject matter expert when it comes to your data. It’s critical that you inform them of any limitations or holes in the data set.
There is a story behind all data, but you must be able to ask it the right questions for that story to come to light. If, for example, your stakeholders want to be able to detect internal bleeding in patients, but despite your best efforts, your model can do no better than random chance, it is your responsibility to share your concerns with your stakeholders. A quick change in direction, investment in new data, or perhaps a suggestion for a feature you didn’t think to engineer are all far more useful than banging your head against the wall.
- Reading Research Papers

This really shouldn’t come as a surprise! Being a Healthcare researcher doesn’t just mean researching your own problem. It’s almost always a good investment of your time to read the research of your peers using machine learning in healthcare. While not as populated as machine learning research in other fields (yet!), the insights of other researchers, even when they are dealing with completely different problems from my own, can often easily be applied to my own work.
If you are serious about becoming a researcher, get serious about reading research. It can be very taxing, in terms of both time and effort, but the net benefit is worth it. I read at least one paper a day. Think of it like this: If the optimal solution were trivial, why would anyone research it? And if someone else has already researched it and found a good solution, why would I need to reinvent the wheel?
If you are serious about becoming a researcher, get serious about reading research.
My Initial Thoughts on the Field
Since this article has already been a decent length, I will try to keep this brief! The below are some of my hot takes from this past month.
- There is a bright future for AI in healthcare! Some of its capabilities are only just being realized, and there are many exciting areas of ML study unique to health data
- The lack of publicly available solutions to certain problems can be confounding, but I believe this will become less of a problem over time
- Domain knowledge is absolutely critical. Having some health data is not enough to be a competent healthcare researcher. Working with health professionals is a must
Conclusion
Thank you for reading my article! Hopefully there will be more in the future. If you liked what you saw, consider following me on LinkedIn or even just Medium.
Let me know your thoughts in the comment section! Are you interested in pursuing research in AI healthcare? Are you a researcher now? If yes, what are your thoughts on the field?