Today we’d like to introduce you to Elizabeth Estes.
Hi Elizabeth, it’s an honor to have you on the platform. Thanks for taking the time to share your story with us – to start maybe you can share some of your backstory with our readers?
What started this for me was actually very personal, but not in the way people might expect.
I met a billionaire, someone who had every resource at his disposal, and despite that, he could not save his wife from a devastating lung disease. That stayed with me. Not just because of the loss, but because it challenged something I think most of us believe. That if you have enough money, enough access, enough connections, you can solve almost anything.
In this particular case, and probably many others, none of that mattered.
Through that experience, I was pulled into the world of medical research, and more specifically the emerging use of artificial intelligence in rare disease. I was asked to get involved in part because I was not from that world. I am not a researcher or a scientist, but I also did not have the same assumptions about how things were supposed to work, and I think that gave me a different lens.
I have always been a builder. My whole life I have been drawn to building things, companies, ideas, systems. I am a bit of a serial builder by nature. But this was different. Because as I got closer to the space, it became deeply personal.
Right after I got involved in this arena, I lost my sister to a rare disease. It was not pulmonary fibrosis, but the parallels were impossible to ignore. Watching her go through that, and then seeing the system from the inside out, you realize how fragmented it is. How hard it is to get clear answers. How disconnected the data is. How much more could be done.
Do not get me wrong, there are incredible people working in healthcare. Brilliant scientists, dedicated clinicians, companies trying to do the right thing. But the system itself is not designed in a way that allows all of that effort to come together. Data lives in silos. Studies are not easily comparable. And one of the most important ways we actually observe disease over time, imaging, is not structured or used in a consistent way for many diseases.
It became clear to me that this was not a science problem. It was an infrastructure problem.
That is what led me to OSIC, the Open Source Imaging Consortium, a 501c3 not for profit organization designed to try to make radical progress for patients, families, and caregivers. OSIC is built differently. Not as a traditional organization, but as a neutral foundation where imaging and clinical data can be brought together, standardized, and actually used to understand disease at scale.
Over the past several years, we have built that foundation with partners around the world. We have helped five companies get FDA clearance for life changing algorithms for those treating patients with rare lung disease. But what is most exciting now is what that foundation enables.
We are moving from being a repository of data into something much more dynamic. An imaging first disease intelligence engine. The way we think about it is that imaging is not just another data type. It is the coordinate system for disease. When you structure it properly and connect it with clinical and longitudinal data, you can start to see how disease begins, how it progresses, and how it responds to treatment in a much more meaningful way.
So for me, this path has been less about entering a field and more about following a problem that I could not ignore. And then doing what I have always done, building something that should have existed but did not.
I am incredibly grateful to have learned from the brilliant and dedicated physician scientists who are on the ground every day fighting rare disease. I have built a lot of things, but nothing more important than OSIC. And nothing more urgent.
Alright, so let’s dig a little deeper into the story – has it been an easy path overall and if not, what were the challenges you’ve had to overcome?
It has not been a smooth road. In many ways, it has been the most complex project I have ever been involved in.
At the beginning, just building the foundation was incredibly challenging. We were trying to create one of the first image anchored repositories for rare disease, which meant navigating technical challenges, but also layers of complexity that most people do not see. Different regulatory frameworks, data sharing restrictions, patient privacy requirements, and cross border considerations all had to be addressed at the same time.
As we grew, the challenges became more human and systemic. Data in healthcare is incredibly valuable, and with that comes hesitation. Institutions are understandably cautious and protective. There are incentives that do not always align and there are concerns around ownership, control, and how data will be used.
So a big part of the journey has been building trust and creating a neutral environment where people feel comfortable contributing, knowing that the goal is to move the entire field forward, not to advantage any one group.
There are also political and social dynamics that come with any system that is trying to change how things have traditionally been done. You are asking people to collaborate in ways they are not used to, and that takes time.
What I have learned is that this is not just a technical problem. It is a coordination problem. It is about aligning incentives, building trust, and creating infrastructure that people believe in enough to participate in.
None of it has been easy. But that is also how you know it matters. And it does. The only way we can ever level the playing field in rare disease is to share responsibly.
Great, so let’s talk business. Can you tell our readers more about what you do and what you think sets you apart from others?
OSIC, the Open Source Imaging Consortium, is a 501c3 not for profit, but we do not think or operate like a traditional organization. We are a charitable organization that acts like a start up. We are building infrastructure for how disease is understood.
Most of healthcare is still organized around fragmented data, disconnected studies, and static views of disease. We are trying to change that by bringing together imaging and clinical data from around the world, standardizing it, normalizing it and turning it into something that can actually be used to understand how disease behaves over time by machine learners, physicians and radiologists.
Nine years ago we started by building one of the largest harmonized imaging datasets in fibrotic lung disease. That alone has already led to real impact, including helping multiple companies achieve FDA clearance for algorithms that are now being used in patient care. But that was never the end goal, it was always the foundation.
What sets OSIC apart is how we think about imaging. Most people treat it as just another input but we see it as the coordinate system for disease. It is the only way to truly observe disease progression in space and time. When you structure it properly and connect it with clinical and longitudinal data, it becomes incredibly powerful. That belief is driving our evolution into what we call an imaging first disease intelligence engine.
We are not just aggregating data. We are building the infrastructure to validate imaging biomarkers, model disease progression, and enable entirely new approaches to clinical trials, including external control arms and earlier detection strategies.
We are also intentionally neutral. We do not compete with our partners. We create a shared environment where academia, industry, and technology companies can work together in a way that is structured, trusted, and scalable. That neutrality is a big part of why people are willing to participate. What I am most proud of is that we have been able to build something that brings together groups that historically have not worked together and actually make it work.
At the end of the day, this is about one thing. If we can move from describing disease to truly understanding it, we can change the speed and trajectory of progress for patients.
What does success mean to you?
I think about success on two levels.
In the short term, success is more operational. Are we hitting the milestones we set and are we continuing to build the platform, grow the data, and keep the organization funded so we can keep moving forward? That part matters, because without it, none of the bigger goals are possible.
But that is not the real definition of success. Real success is whether what we are building actually changes patient care. Are the tools, models, and insights coming out of OSIC being used in the clinic? Are they helping physicians make better decisions? Are they helping patients get diagnosed earlier or treated more effectively? We are starting to see early signs of that in rare lung disease, which is incredibly meaningful.
The bigger vision of success is that what we have built is not limited to one disease area. That the structure itself can be used across organs and across diseases. That we can take this imaging anchored approach and apply it to other rare diseases where imaging plays a critical role in diagnosis, prognosis, or response to therapy.
If we can do that, if we can move from one disease to many and create a repeatable model that actually improves how disease is understood and treated, that is success.
Contact Info:
- Website: https://www.osicild.org
- LinkedIn: @osicild
