Finding Impact Through AI in Healthcare: My Journey to Inception Health

Throughout my career, I've been driven by a fundamental question: where can I make the most meaningful impact? The answer to this question led me first to surgery and then surgical oncology, where the day-to-day impact is tangible and profound. There is no doubt when I finish a complex surgery and witness a patient's path to recovery that I have been able to make an impact. These moments in the operating room and clinic, developing relationships with patients and families during their cancer journey, represent healthcare at its most personal level.

Through the course of my training and early time as a faculty surgeon, I have been fortunate to learn from mentors who showed me that a surgeon's impact could extend beyond the operating room through academic contributions, leadership, and advancing the science of surgery itself.

Bridging Clinical Care and Data Science

This realization prompted me to pursue an advanced degree in epidemiology during my general surgery training, with a specific focus on predictive analytics and machine learning. This experience opened my eyes to an untapped resource: the tremendous volume of data collected during routine patient care that remains largely underutilized. What became clear was that this data contained insights that could transform healthcare delivery -- making it more efficient, more effective, and more accessible. I began to see that improving healthcare delivery systems could create pathways to better health for entire populations. My research laboratory evolved to focus on artificial intelligence applications in healthcare, producing publications, presentations, and creating opportunities to mentor trainees interested in this emerging field. However, I, like so many others, encountered what many researchers face: the "last mile problem" of implementation.

From Research to Real-World Impact

How do we translate promising AI research into tools that actually improve patient care? How can we ensure that these technologies are implemented safely and rigorously? And shouldn't frontline healthcare workers -- those who understand how patient care can be improved -- be leading this transformation? These questions ultimately led me to Inception Health, where I now serve as Artificial Intelligence Lead. In this role, my goal is to help bridge the gap between cutting-edge AI research and practical healthcare applications, working directly with clinicians and patients to develop solutions that address their most pressing needs. Inception Health is a unique place to make this happen. I have worked closely with Brad Crotty, MD MPH and Melek Somai, MD MPH since I joined the Medical College of Wisconsin in 2021. They have been at the cutting edge of digital health innovation and AI way before it was “cool” (I think there were very few others building clinical chatbots with GPT-2…). Their vision is brought to life by an incredible team of software engineers and digital health experts -- it is this rare combination of domain expertise, technical excellence, and shared purpose that makes the work at Inception Health both meaningful and scalable.

The AI Opportunity in Healthcare

In healthcare, innovation often moves at a cautious pace (evident in my clinical day-to-day reality, where I still use a pager and need to remember my fax number). This is despite major discoveries happening all the time that can have practice-changing implications. For example, the time it can take a clinical trial to move into routine care takes an average of 7 years. This careful, methodical, and slow tempo is intentional, based on the absolute need to prioritize safety, evidence, and consensus over speed. But as we see the velocity of AI innovation occurring in other industries, especially in areas where there is overlap with healthcare, we need to adapt. The question is how we bring the right kind of acceleration to healthcare without sacrificing trust. At Inception Health, we're working on driving that change. We recognize that AI innovation in healthcare requires deep clinical understanding paired with technical expertise. It demands rigorous validation while maintaining the agility to iterate quickly. Most importantly, it requires keeping patients at the center of everything we do.

AI at Inception Health

As AI Lead, I will focus on guiding the development of frameworks that allow us to use AI responsibly and effectively. Specifically, this includes: 1) empowering our workforce to use and understand AI tools through training and education; 2) creating AI applications that utilize state-of-the-art models and techniques to address real clinical needs; 3) researching and evaluating AI applications when implemented in real world settings; 4) partnering with AI innovators to learn and co-develop; 5) using these learnings to guide health system AI policy and governance. In the coming months, I'll be building teams and processes that bring together clinical and technical expertise to develop AI applications that can improve patient care. We'll be establishing methodologies to rigorously evaluate these technologies, ensuring they deliver on their promise to improve health outcomes.

Join the Conversation

I will share our progress, challenges, and insights regularly as we navigate this exciting frontier. I invite you to follow along and engage in this important conversation about how we can responsibly use AI to make an impact. If you're working on similar problems -- or simply curious about how this all comes together in practice -- let's connect. AI innovation doesn't happen in silos and there is so much to learn together.