Naomi Yudanin, PhD, is a healthcare technology executive with nearly 15 years of experience leading data, AI, and product development across the care continuum. As Head of Data & AI at Junction, she drives AI-enabled analytics and commercial strategy for the company’s diagnostics platform, transforming data from devices, labs, and clinical systems into actionable insights.
Previously, she has held executive roles at the intersection of data, product, and care delivery, building scalable infrastructure that powers innovation, operational intelligence, and measurable impact in healthcare.
How did you get started in healthcare? Have you always been passionate about this area, or did it happen by chance?
My path into healthcare was driven by intellectual curiosity more than anything else. I started in academia with a PhD and postdoc in clinical informatics, initially drawn to the challenge of trying to model what seemed like an inherently un-modelable system; biology remains the hairiest math problem I’ve ever encountered. As my work in clinical machine learning applications progressed, I found myself in the right place at the right time: the NYC health tech ecosystem was growing rapidly, and AI was exploding across industries (particularly in health).
What began as an academic pursuit evolved into something more strategic. I was fortunate to have picked a niche that positioned me as one of the few people who could straddle both healthcare and technology, translating fluently between the two worlds. So while passion certainly developed along the way, I’d say it started with curiosity and was shaped significantly by timing and circumstance.
As we all know, AI is at the forefront of innovation these days. Can you tell us about your current research and the problems you’re trying to solve?
Funny enough, what we currently call AI is actually a small slice of machine learning, which itself is part of information theory: a field that’s been around for nearly a century. We’ve been using machine learning and statistical modeling methods in healthcare for years now. My own work has always focused on applying these methods to longitudinal data, like what clinical records capture, particularly when that data is multi-dimensional, comprising different kinds of observations such as vitals, lab results, or genetic sequences.
At Junction, our unified diagnostics platform powers the exchange of clinical lab results and wearables data, which gives us a unique opportunity to overlay these modalities and develop a more cohesive understanding of a patient’s health over time, often before anything even shows up in their EHR. We’re working to leverage this for better predictive insights and earlier disease detection and intervention, with much less friction compared to traditional in-office care.
You’ve mentioned often being the only woman in the room while working in engineering and tech. What has that experience been like for you, and how has it shaped your leadership style?
Being the only woman in the room has been a recurring theme throughout my career in engineering and tech, and it’s definitely shaped how I approach leadership. The experience has made me hyper aware of the biases women face in this field, both the overt and the subtle ones, and I’ve made it a priority to actively address and call them out when I encounter them or see others affected by them.
More importantly, it’s pushed me to be intentional about creating space for other women. I proactively seek out, hire, and mentor women in and around tech because I know how isolating it can feel to navigate these spaces alone. I’ve been privileged to meet some phenomenally talented female colleagues who are pioneers in health-focused AI, and I hope to see more of us with time. Representation matters, and I’ve seen firsthand how much of a difference it makes when women have advocates and allies who understand the unique challenges we face. Building that support system and being that support for others has become a core part of how I lead.
Personally or professionally, what might the WBL network be surprised to know about you?
I had a relatively successful calligraphy business that kept me afloat in my postdoc days. That experience fueled what’s now become an obsession with anything and everything visual. I’m a big data visualization nerd with unreasonably strong opinions about typography! It’s one of those unexpected throughlines from my personal life to my career, where the creative and analytical sides come together. I think it’s made me better at communicating complex information in more intuitive ways.
