Capital follows more than technological novelty. In healthcare, investors look for urgent problems, credible routes into clinical practice and teams capable of changing course when the original plan no longer works. Dalibor Marijanović of Vesna Deeptech Venture Fund identifies the core value of AI4Health.Cro as giving technology teams access to healthcare processes that would otherwise remain beyond their reach.
Artificial intelligence in healthcare is attracting attention across a wide range of applications. Yet attention alone does not make a technology investable. For Dalibor Marijanović, partner at Vesna Deeptech Venture Fund, the first test is more direct: can the innovation succeed, enter the healthcare system and ultimately generate a return?
“The area of healthcare that attracts capital is the one that can return it,” Marijanović says.
This means looking beyond the novelty of an algorithm or device. The strongest investment opportunities are often those that address an urgent healthcare problem or apply technologies that can enter clinical practice relatively quickly.
Radiology, teleradiology and AI-supported radiology are among the fields where Marijanović expects rapid change. He also sees opportunities in psychology, prevention, nutrition and supplements, including devices and software that monitor health, support well-being and help people align their everyday habits with what their bodies need.
But the most visible trend is not necessarily where the greatest unrealised value lies.
Beyond the monitoring hype
Much of the current attention is focused on increasingly sophisticated sensors for monitoring vital functions. These technologies can provide the foundation for new software and services directed at individual users.
However, Marijanović distinguishes between consumer-facing monitoring and solutions designed for clinics, hospitals and other healthcare providers. In his view, the latter remains insufficiently developed and used.
The greater opportunity may lie in applying technology through physician-led patient monitoring. Rather than functioning as a parallel system outside healthcare, technology should help doctors deliver care more quickly and improve its quality.
This distinction matters for investment. Collecting more data is not necessarily the same as creating clinical value. The opportunity becomes stronger when monitoring is connected to medical expertise and incorporated into the way healthcare professionals care for patients.
The team is not a cliché
Investors are often said to invest in the team before they invest in the project. For Marijanović, this is not simply a familiar phrase from the investment world.
“A strong team is not a cliché; it is an axiom,” he says.
A capable team cannot guarantee success, but a weak team is likely to create problems later. Strong teams can respond when the original assumptions behind a project no longer hold. They can pivot, change the business model and, if necessary, change the product itself.
This capacity to adapt is particularly important at an early stage, when neither the technology nor the market strategy is fixed. The team may therefore be the most important factor in an investment decision, not because the initial idea is irrelevant, but because the people behind it must be capable of transforming that idea as evidence, users, and market conditions demand.
From scientific output to an investment opportunity
Vesna Deeptech Venture Fund invests at an early stage, at the point where science and business meet. This is often the stage at which scientific output must begin its transition towards the market.
A research project may already have produced valuable technology, but technology alone does not constitute an investable company. Scientific projects frequently begin without a dedicated team, a business model or a market strategy. Investment and commercial support can help turn such projects into opportunities capable of reaching users and markets.
This gap between scientific achievement and commercial development is especially relevant in Europe. Marijanović points to the substantial public funding made available for science, including through Horizon 2020. What Europe has lacked, in his assessment, is a comparable level of funding for commercialising the results of that research.
European Innovation Council programmes are beginning to address this imbalance, but Marijanović argues that the balance should shift further towards commercialisation.
Not every researcher needs to become an entrepreneur. Scientists who dedicate their work to advancing knowledge should continue doing so. Research funding also remains valuable for building teams and acquiring instruments and equipment.
But researchers who want their work to reach the market need access to capabilities that scientific projects do not automatically provide. For Vesna, the essential requirements are a genuine intention to enter the market and a scientific basis underpinning the proposed technology.
Innovation needs access
Healthcare presents a particular difficulty for technology teams. It is a highly regulated sector in which access to data and operational processes is often limited to people who are already inside the healthcare system.
This creates a barrier for creative technologists who may have relevant ideas but cannot easily observe healthcare processes, work with appropriate data or understand how their solutions would function in practice. As a result, part of their innovation potential may be lost before it can be tested.
Marijanović identifies this as a central value of AI4Health.Cro: it gives technology teams access to healthcare processes that would otherwise be difficult to reach.
The model may not be equally necessary in every industry. In less regulated sectors, innovators can often approach users and markets without the same structural barriers. But wherever entry into a sector depends on controlled data, specialised processes or institutional access, an initiative such as AI4Health.Cro can provide an important bridge.
Investability begins before the pitch
The investment case for healthcare AI is therefore built long before a team enters a room to meet investors. It begins with an urgent problem and a credible understanding of how the technology can be used. It requires a team capable of adapting, a scientific foundation that can withstand scrutiny and a route into the healthcare processes where value must ultimately be demonstrated.
The most promising innovation is not necessarily the technology generating the most attention. It may instead be the solution that connects scientific quality, clinical expertise and market purpose—and has the team needed to carry it across the barriers between them.