How Leander Maerkisch Is Changing Radiology With AI-Driven Preventive Screening

When a radiologist examines a CT scan of a patient's lungs, they're typically only looking for signs of lung-related issues. However, that same scan could reveal early signs of osteoporosis, cardiovascular disease, or other conditions. What if there was a way to consistently identify these signs and make life-saving diagnoses with imaging that's already completed — all without requiring any additional effort from overworked healthcare professionals?

This is precisely what "opportunistic AI screening" aims to accomplish, and it's what forms the basis of Floy, a healthcare AI company co-founded by Leander Maerkisch.

As a Forbes 30 Under 30 honoree with a background in business and self-taught programming, Maerkisch created Floy to transform routine radiological examinations into comprehensive health assessments. By leveraging AI to analyze the millions of pixels in standard medical scans, Floy extracts valuable health insights that would otherwise remain undetected. It's an approach that has attracted over $14 million in funding and has the potential to reshape preventive medicine through radiology.

The Birth of Floy

Maerkisch's mission was born out of a personal frustration with the healthcare system: "My mother walked around for three weeks with a broken ankle before doctors finally diagnosed her with osteoporosis," he shares. "With better diagnostics, this suffering could have been prevented entirely."

His mother's story is far from uncommon. Unfortunately, many conditions are detected only after they've caused significant damage, despite visible warning signs being visible in prior imaging.

While studying at WHU – Otto Beisheim School of Management, Maerkisch formed a friendship with Benedikt Schneider, whose soccer career had been cut short by a meniscus tear. Despite their lack of medical training, they unearthed a fundamental problem in the radiology AI market: Competitors were developing sophisticated algorithms but struggling with adoption because they focused on replicating radiologists' existing workflows rather than expanding diagnostic capabilities.

"Most companies were building software to make radiologists more efficient," Maerkisch notes. "We saw an untapped opportunity in partnering with radiologists instead of replacing them — offering patients additional insights beyond the primary examination."

Engineering Floy

As the technical mind behind Floy, Maerkisch faced the considerable challenge of developing AI models that could detect multiple conditions from a single scan — without disrupting existing radiological workflows. This required innovative approaches to both algorithm development and system integration.

"Traditional machine learning models are condition-specific, requiring extensive labeled data for each particular disease," explains Maerkisch. "We needed something more flexible and efficient."

The breakthrough came through a collaboration with the German Cancer Research Institute, where Maerkisch led the development of a 3D foundation model for medical imaging. Similar to how large language models understand text, this model could comprehend the underlying structure and patterns in medical scans, enabling the faster development of detection algorithms through fine-tuning — rather than building each one from scratch.

This dramatically accelerated Floy's ability to deploy new screening capabilities. What once took months of development could now be accomplished in weeks, allowing the platform to expand from bone density assessment to detecting early signs of cardiovascular disease, cancer, and other issues.

Navigating Healthcare Regulations

Unlike most AI startups, medical AI technology must pass strict regulatory approvals before clinical use. This is already a challenging barrier to overcome, but simultaneous to Floy's launch, Europe was transitioning from the Medical Device Directive (MDD) to the more stringent Medical Device Regulation (MDR), creating what Maerkisch describes as "a perfect regulatory storm."

"The certification process practically doubled in complexity overnight," he recalls. "Regulatory bodies were overwhelmed, creating enormous backlogs, while the new requirements remained ambiguous since they hadn't been fully tested."

Rather than viewing these regulations as obstacles, Maerkisch's team engineered compliance into their development process. This included the creation of automated documentation systems that would generate regulatory evidence alongside the AI models themselves. In doing so, Maerkisch and his team not only satisfied regulatory requirements but actually accelerated development by catching potential issues earlier.

As a result, Floy secured CE marking in record time despite this regulatory upheaval. By embedding quality controls directly into their development pipeline, he and his team transformed regulatory compliance from a bottleneck into a competitive advantage.

Building a Patient-Centric Business Model

While the technical innovations behind Floy's AI were plenty notable, Maerkisch's most disruptive contribution may have been the business model he engineered around the technology. Unlike competitors who sold software directly to hospitals as efficiency tools, Floy created a patient-centered approach where individuals could purchase AI-powered insights as an add-on to their existing radiology appointments.

"We realized that convenience for patients was crucial," Maerkisch explains. "Why should someone have to schedule multiple appointments, pay for separate scans, and take additional time off work when we could extract more valuable information from an image they've done?"

This model would effectively address the practical challenges that had frustrated both patients and providers. Rather than requiring additional visits for more screenings, Floy could integrate seamlessly into existing appointments and give patients access to comprehensive health insights without any additional effort. Patients could opt for additional AI analysis at the point of care, radiologists would receive modest compensation for their expertise in reviewing these additional findings, and the results would be incorporated into a single comprehensive report.

This approach transformed what Maerkisch calls "opportunistic screening" into a practical reality. For example, a patient undergoing a routine chest CT for lung concerns could simultaneously receive an assessment of their bone density and cardiovascular health from the same scan. In doing so, it allows for the identification of serious conditions much earlier than traditional sequential screening approaches — all without the need for additional appointments, radiation exposure, or time away from work and family.

The Future of AI in Healthcare and Leander's Vision

Following his success with Floy, Maerkisch has set his sights on an even more ambitious goal: reimagining primary care through AI. His new venture, Starlife, aims to bring world-class medical expertise directly into patients' homes through a combination of advanced diagnostics, AI analysis, and personalized care planning.

"The lessons from Floy showed us that healthcare shouldn't require patients to navigate a fragmented system of specialists and appointments," Maerkisch notes. "With the right technology, we can transform how people experience healthcare — making it proactive, continuous, and centered around their lives rather than hospital visits."

While details regarding Starlife remain under wraps, it's poised to expand on Floy's approach by continuously monitoring health, spotting concerning patterns early, and connecting patients with specialists at precisely the right time.

As Maerkisch continues pushing the boundaries of AI in healthcare, he's contributing to a future where truly preventive medicine isn't just an aspiration but a practical reality — one where cases like his mother's delayed osteoporosis diagnosis become increasingly rare. By combining technical expertise with a deep understanding of healthcare's complex structures, he's demonstrating that medical progress doesn't come from just using clever algorithms but fundamentally rethinking how patients experience healthcare.