Expanding to New Healthcare Markets: The Growing Impact of Medical AI

Artificial intelligence in medicine is most often associated with detecting abnormalities in medical images, analyzing test results, and helping doctors make diagnoses. But lately, I’ve been encountering more and more developments that go far beyond these applications and challenge the way we think about what medical technology can achieve. Some are so ambitious that their potential impact could extend well beyond healthcare, affecting entirely different areas of our lives and giving rise to new technologies, business models, and even markets that don’t yet exist.

Why Rare Diseases Are Becoming Attractive to Medical Startups

One of the most interesting areas I’ve been following lately is drug discovery, the search for and development of new drug compounds using computational methods and artificial intelligence. DeepMind’s work on AlphaFold has played a major role in advancing this field. Its database of more than 200 million predicted protein structures has given researchers a wealth of information to explore potential molecular interactions and has also opened new opportunities for startups targeting specific medical conditions.

Suppose someone wants to build a startup to develop a treatment for a relatively rare disease that affects only a small percentage of women in a particular age group. The first question: why should they target such a limited market when they could pursue a much more common condition? But even after narrowing the global population down to this specific group, they could still have thousands of potential patients. And if their treatment requires patients to take a daily medication for ten years or for the rest of their lives, even that relatively small patient population could represent a substantial market.

A commercially viable pharmaceutical business doesn’t necessarily need tens of millions of customers. Of course, there is an enormous gap between estimating a potential market and generating actual revenue, including the costs of research, clinical trials, manufacturing, and regulatory approval. Still, a startup can focus on a single underserved disease and build an entire research program around it.

For example, Recursion Pharmaceuticals is a US-based biotechnology company developing AI-driven treatments for diseases with significant unmet medical needs. One of its investigational drugs, REC-4881, targets familial adenomatous polyposis, a rare inherited condition affecting an estimated 50,000 people across the US and five major European countries. The company has reported encouraging Phase 1b/2 trial results, including a reduction in abnormal tissue growths among patients.

Virtual Labs as a New Business Model

It is a real challenge to find a molecule that can target a protein associated with a particular disease. This requires screening vast numbers of chemical compounds to identify those that can bind to the target protein, alter its activity, or affect related biological processes. And it’s not just the chemical composition that matters. The three-dimensional structure of each molecule plays a crucial role in determining how these interactions occur.

Testing every possible combination in a physical laboratory is practically impossible. Even with substantial financial resources, conducting that many experiments would take far too long. This is why part of the research process is moving into virtual environments, where computational models and simulations can predict molecular interactions and identify the most promising compounds for further testing. This approach reduces the number of experiments needed in the lab, although the predictions still have to be validated through physical testing.

An entire ecosystem of startups with different business models is emerging around these technologies. Some build molecular simulation infrastructure and make it available to pharmaceutical companies and research teams. Others use existing tools to develop their own drug candidates. It’s an interesting example of how a single technological advancement can create business opportunities at multiple levels of the market. A company can build a commercially successful business without ever bringing its own drug to market, provided its technology proves valuable to other developers.

A great example is Isomorphic Labs, which develops AI technologies for drug discovery and molecular design. The company has established research partnerships with Novartis and Eli Lilly and, in 2025, expanded its collaboration with Novartis to include additional drug discovery programs. It illustrates how the technology used to discover and design molecules can itself serve as the foundation for a business working with major pharmaceutical companies.

A Personalized Pill in Twenty Minutes

Some startups are taking the idea of drug discovery even further. I recently met a team working on technologies related to skin cancer. They are interested in identifying genetic predispositions to the disease and exploring ways to treat it at the DNA level. Rather than developing a drug for a broad group of patients, they are investigating whether treatments could be tailored to an individual’s genetic profile.

The founders described a particularly ambitious vision of the future. Imagine walking into a clinic, providing a saliva sample, and having a device analyze your DNA to prepare a drug specifically designed for you. In their vision, the entire process could one day take around twenty minutes and cost as little as $100. Of course, this is still a technological ambition rather than an available medical service, and turning it into reality would require overcoming enormous scientific, manufacturing, and regulatory challenges.

Still, conversations like this reveal how much the ambitions of medical entrepreneurs are changing. Instead of simply improving existing diagnostic procedures, they are asking whether the process of developing treatments itself could be fundamentally redesigned. I find it fascinating how advances in computational technology are encouraging founders to reconsider processes that have remained largely unchanged for decades.

Why Surgical Robotics Startups Often Build to Be Acquired

While computational technologies allow small teams to explore vast numbers of possibilities in drug discovery, surgical robotics demands an entirely different level of resources. Building a system capable of performing surgical procedures requires sophisticated mechanical engineering, precise motion control, extensive testing, and access to clinical environments. Developing a reliable robotic surgical arm can take ten to fifteen years, while a single installation may cost millions of dollars. The potential customer base is also limited, and manufacturers must provide years of maintenance and technical support. For a small startup, this makes surgical robotics an exceptionally complex and capital-intensive business.

That is why some teams build their strategies around eventual acquisition by a major medical technology company. Their goal is to develop a convincing technological concept and demonstrate its potential. A startup might, for example, build a prototype robotic arm capable of performing certain procedures on a medical mannequin, even though it is far from ready to operate on a real patient. Meanwhile, the team actively engages with investors, attends industry conferences, and builds relationships with potential corporate buyers, as we often see while working with medical events at Scryon. Essentially, they are trying to sell the promise of a technology before it becomes a finished product.

At first, I found this approach rather strange. Why develop medical equipment if you have no intention of bringing it to market yourself? But there is a business logic behind it. A small team may have talented engineers and a promising idea, yet lack the resources for years of testing, access to hospitals, and manufacturing at scale. A large corporation can provide that infrastructure by acquiring the startup at a relatively early stage.

This strategy illustrates how small teams and large corporations can complement each other: one focuses on developing and testing new ideas, while the other has the resources to turn them into viable medical products. And a company’s size does not necessarily reflect the complexity of the work behind its technology. 

I have seen this firsthand through projects at Keymakr, where we have worked with developers ranging from small startups to one of the world’s leading medical equipment manufacturers. Regardless of the company’s size, these projects involve painstaking work to refine algorithms, prepare medical datasets, and verify the accuracy of annotations against clinical requirements. 

Beyond the Original Idea

Any company investigating a specific biological mechanism might one day develop a technology that transforms entirely unrelated industries. This has far-reaching implications, up to and including radical increases in longevity and completely transforming society as we know it.

Perhaps this is what fascinates me most about medical startups today. With the help of AI, small teams are taking on challenges that, until recently, would have required the resources of major corporations, often approaching them from unexpected angles. Many of these ideas will never make it into practice, but entrepreneurs don’t necessarily need to know in advance how large a market a successful technology might eventually create. Sometimes the most interesting opportunities emerge only after the original scientific problem has been solved.