Bridging Health Sciences and Artificial Intelligence
There’s a moment most people don’t talk about — the one right before you decide to pivot. Not dramatically. Not with a grand announcement. Just a quiet realization: I want to do more than this.
For Anandita Mahika, that moment didn’t come with a computer science degree in hand. It came from a Bachelor of Health Sciences, a curiosity about medical imaging and a growing fascination with how machine learning could actually change patient care.
And here’s the thing: she didn’t wait until she “fit the mould” to go for it.
Not a Typical CS Story (And That’s the Point)
Anandita didn’t come from a traditional computer science background. She graduated with a Bachelor of Health Sciences with a focus on bioinformatics from the University of Calgary, where her early exposure to research (particularly in medical imaging and machine learning) sparked something bigger: not expertise or mastery, but interest.
“I didn’t really have a CS background,” she admits. “But working on those projects made me realize I wanted more advanced training in this space.”
That’s where the decision starts to shift: from “Can I do this?” to “Where can I learn this properly?”
Choosing a Program That Actually Bridges the Gap
What drew her to the MScAC program wasn’t just the reputation (though let’s be honest, that helps). It was the structure. A mix of research and industry, theory and application, coursework and real-world exposure! And most importantly, a clear pathway into AI in healthcare, a space she already knew she cared about.
For someone coming from outside traditional computer science, that balance mattered. “I wanted something that would help me build strong technical foundations but still stay connected to healthcare.”
From “I Think I Like This” to “I Know What I Want to Build”
Before the program, Anandita’s career goals were broad: healthcare, yes, machine learning, probably, but without a clear sense of specific roles. That kind of ambiguity is more common than people often admit. It wasn’t until she moved through both the coursework and her internship that things started to come into focus. Concepts like neural networks, model deployment and clinical workflows shifted from abstract ideas to something more grounded and directional.
Now, her focus is much clearer. She’s interested in building end-to-end machine learning systems for clinical environments, a shift from exploring possibilities to actively owning a space within them.
Learning Hits Different When It’s Real
Her internship at the Princess Margaret Cancer Centre wasn’t just another line on a resume; it was where everything became real, quickly. She worked on machine learning pipelines for medical imaging, focusing on improving treatment planning workflows in clinical settings. These systems involved building and evaluating models that supported decision-making in healthcare contexts, where accuracy, reliability and interpretability were critical. The work moved beyond theory or neatly prepared datasets and into real clinical data, real constraints and real impact.
“You don’t really think about things like reliability or interpretability until you’re actually building something that could be used in practice.”
That’s the difference between learning about machine learning and actually doing it.
Reflections and Advice for Future Students
Being in Toronto added another layer to the experience. As one of North America’s largest tech hubs, the city offered more than just scale. What stood out to Anandita was the sense of collaboration. Events, networking opportunities and conversations across disciplines all shaped how she thought about growth in AI, especially in healthcare, where no one works in isolation. And at a place like University Health Network (UHN), where her internship took place, the scale of impact becomes hard to ignore.
That perspective also shaped how she approached the program itself. It’s easy to focus on the internship as the defining milestone, but Anandita points to the coursework as just as important.
Graduate courses aren’t spoon-fed; they’re open-ended, project-driven and ultimately reflect on the effort you put into them. At the same time, the internship isn’t a lighter counterpart; it’s the continuation of that learning in a different environment, where problems are less structured and more applied.
“If you’re intentional, those projects can become the skills you need, or even something you publish.”
