Munich startup dehaze targets earlier detection and up to $800b in savings by finally making sense of healthcare’s unused data.
Healthcare has never had more data, and yet, it still misses the moment that matters most. Every patient generates a trail: lab results, scans, prescriptions, histories. However, in practice, doctors only have the time and tools to review less than 3% of that information before making a decision. The rest sits there – unused, unseen and, in some cases, quietly consequential. That gap is where chronic disease slips through.
More than 31% of chronic conditions go undetected at the stage where they could still be prevented, slowed, or treated more cheaply. By the time they are caught, the intervention is often more complex, more invasive and more expensive. Munich-based startup dehaze is built around a simple idea: what if we could actually use the other 97%?
dehaze has raised $3.5 million (€3.2 million) in a seed round led by YZR Capital and DN Capital, with participation from Angel Invest, ZOHO and Better Ventures [1]. The funding will be used to scale its engineering, research and commercial teams, as well as accelerate product development as international demand grows.
The ambition is not small. The company is working toward building what it calls a “foundational AI model” for chronic disease detection, software designed to scan vast amounts of patient data and flag early warning signs before they escalate.
“Chronic disease is the biggest and most expensive problem in healthcare, and it has been unsolved for one simple reason: the data exists, but no one has been able to use it,” said Marius Klages, co-founder and CEO of dehaze. “Doctors see less than 3% of what’s available before making a decision, and as a result, roughly a third of chronic diseases go undetected until it’s too late or too expensive to act. That is the gap we close.”
If most AI in healthcare feels abstract, dehaze’s pitch is surprisingly concrete. Think of it like a second brain that never gets tired. It scans the full scope of patient data – past records, patterns and signals – and looks for combinations that suggest risk. Not just “this looks unusual,” but “this may lead to something, and here’s why.”
Telling “here’s why” matters. One of the persistent criticisms of AI in medicine is that it can feel like a black box. dehaze is trying to move in the opposite direction, offering explanations that clinicians and insurers can act on, rather than just predictions.
As Gülsah Wilke of DN Capital put it: “dehaze is not an LLM for medicine… [it is] the unglamorous but vitally important and scientifically rigorous layer that global healthcare has been missing.”
The first customers lining up are not necessarily hospitals, but payers – health insurers and systems that shoulder the financial burden of care. That makes sense. Chronic diseases account for more than $8 trillion in annual healthcare spending globally and are responsible for roughly seven out of ten deaths [2]. Even small improvements in early detection can translate into significant savings.
dehaze says its platform can reduce annual health spending by up to 10%. In a category this large, that starts to look less like optimization and more like structural change. When insurers invest in earlier detection, they are (intentionally or not) nudging the system toward prevention. And prevention, long discussed but rarely implemented at scale, is where longevity moves from theory to practice.
Investors see dehaze as part of a broader wave of companies trying to rebuild healthcare’s foundations using AI.
“At YZR Capital, we focus on identifying global multi-billion USD opportunities within healthcare that can be tackled with cutting-edge AI,” said Markus Feuerecker. “With dehaze this is happening at a complete system level… for effective chronic disease prevention.”
The company already has a growing international pipeline, with plans to expand both its technical and commercial teams. Future modules will go beyond detection, offering recommendations on the next best action, essentially closing the loop from insight to intervention.
For Klages, the pace of adoption is a signal in itself. “The speed at which payers are signing with us confirms what we believed when we started: this is a category that will be defined over the next few years, and dehaze is going to define it.”
If you look closer, dehaze’s story is not just about AI or funding rounds; it’s about timing. Longevity, at its core, is not only about living longer but about staying healthier for longer. And that depends less on breakthrough treatments and more on catching risks early, before they compound.
In that sense, the real promise of tools like dehaze is not that they make healthcare more high-tech, but that they make it more attentive. They give systems the ability to notice what would otherwise be missed.
Of course, technology alone won’t fix healthcare. Data still needs to be shared responsibly, systems need to be aligned and clinicians need to trust what they’re using. If those pieces begin to come together, the impact could be profound.
If dehaze is right, that shift – from missed signals to early action – could unlock not just billions in savings, but something harder to quantify: more years lived well.
[1] https://www.dehaze.de/press/dehaze-raises-%E2%82%AC3.2-million-to-build-the-foundational-ai-model-for-chronic-disease-detection%2C-unlocking-up-to-%24800bn-savings-in-the-world%E2%80%99s-largest-healthcare-cost-category
[2] https://www.who.int/publications/i/item/9789240104495
