What Defines Real AI Success in Healthcare? Conrad Gudmundson on Health Innovation Matters
Live from the floor of ViVE 2026, we joined the Michael Levin-Epstein on the Health Innovation Matters podcast for a conversation about one of the most pressing topics in our industry: What does real AI success look like in healthcare today?
In the episode, our Chief Commercial Officer, Conrad Gudmundson, explained that in an industry where AI is now “table stakes,” the definition of success has to evolve.
“I am no longer interested really at all in a company that says they’re an AI company,” Conrad stated. “Lucem [Health] is a clinical programs company… We are population-level precision medicine.”
From Technology to Clinical Outcomes
True AI success isn’t measured by the algorithm, but by the clinical outcomes it enables. It requires a fundamental shift in focus, from implementing technology to streamlining the diagnostic process.
“We are obsessed with the next diagnostic action,” Conrad explained. “We want to streamline and ultimately revolutionize how patients ultimately get a diagnosis.”
The model is elegantly straightforward: AI scans millions of existing patient records—including unstructured notes—to find individuals at high risk for serious, underdiagnosed diseases. The system then pinpoints the single most effective next step, whether that’s a specific lab test or an imaging study, that can guide a person toward a life-changing early diagnosis.
A New Standard of Care in Action
Consider liver disease, where an estimated 80-90% of cases go undiagnosed. With new treatments now available, early identification is more critical than ever, but primary care and specialist schedules are already overwhelmed.
“What we need to change,” Conrad noted, “is we need a way to identify the patients from existing retrospective EHR data that are living with the disease. And then we need to, in an automated way, guide them to that next diagnostic action.”
This is AI success in practice. By identifying at-risk patients and prompting them to get a simple blood test or FibroScan before they see a clinician, a complete diagnostic picture is ready for the first appointment. This empowers clinicians and prevents patients from getting “bounced around” the system.
This tangible impact is forcing the industry to re-evaluate what constitutes the standard of care. It raises a question Conrad is hearing more and more from forward-thinking health systems:
“When will not using AI be considered malpractice?”
The Moneyball Playbook for Healthcare
The data-driven philosophy we shared is the essence of our “Moneyball for Healthcare” strategy—using smart analytics to find opportunities to make a real clinical impact. For a deeper dive, you can listen to the full episode here.
Ready to build your own strategy for AI success? Download our white paper, “Moneyball for Healthcare,” to learn how to activate your data and build winning clinical programs.
