"As a deeptech healthcare founder, you must navigate steep learning curves across clinical, regulatory, reimbursement, and market landscapes. You need to embrace uncertainty while maintaining thoughtful, strategic roadmaps for each. It’s about mastering the complexity of commercialisation and scale as much as it’s about science."

Tim Mahoney, Founder of Fluent

What’s different about founding a deep-tech startup in health

The obvious point is that cycles are longer and harder. Regulatory pathways (be it FDA or EMA), clinical validation, and reimbursement sit between a founder and their first real customer or commercial agreement, there's no market-ready MVP-in-six-months shortcut like unregulated software categories.

Capital intensity and patience are required on both sides. Timelines of 5–15 years from lab to market are common in deep tech generally (vs 2-5 years for a standard startup), and health is no exception once trials are involved. Hospitals use long procurement cycles by design, often around 9-15 months, so runway needs to be deployed carefully. 

That timeframe is coming down, however, and we’re seeing AI accelerate modelling capability and improve the accuracy of projections. The increasing appetite of investors also means that there are more grants and capital available to founders than ever before, helping accelerate R&D and connect the right people and institutions. A benefit that has played out in Founders Factory’s programmes with Northwestern Medicine, MSWA and Innovate UK. 

The IP-moat logic is also different. Long-term advantage comes from proprietary data, hard science and regulatory clearance rather than speed of iteration. In healthcare access to patient data is locked away within larger institutions’ databases and clinical codes. Often those institutions won’t take a meeting with a supplier unless their data accreditation meets a certain level, be it HIPAA compliance in the US or mandatory Data Protection Impact Assessments in the EU. The fastest route to compliance is often building alongside or within the institutions you aim to sell to, be that as an academic spin-out or through an accelerator programme or partnership.

In truth, many deep-tech startups are academic spinouts primarily because it takes someone with deep expertise within the subject area to build a business around it. Universities are generating deep-tech startups en masse, in the UK nearly 3,000 spinouts have emerged from universities raising towards £18bn in follow-on. 

Founders Factory has been witnessing the acceleration of academic research to commercial company through Creator Fund, recently raising $56 million to continue its investment in PhD students and researchers whilst in university ecosystems across Europe. The fund has successfully invested in over 60 ventures with two companies raising over $100 million and one exit. As founder Jamie McFarlane says: “The world's biggest problems are being solved in European university labs… This fund allows us to support more of these founders across Europe and help translate scientific breakthroughs into companies”

Without programmes like Creator Fund the skillset transfer from academia often doesn’t get supported for commercialisation and go-to-market, so there’s a new, steep learning curve alongside the technical knowledge. 

"Building in healthcare - particularly if you're tackling anything related to direct patient care - requires humility, patience, and deep partnership. Humility to navigate the steep learning curves of technical and regulatory complexity, patience around the daily realities of human-centric operations, and deep partnership with clinical leaders who share your vision."

Carine Carmy, Founder of Origin

The core traits and skills founders need

Deep-tech’s gap from concept to scale is closing through technology, investment and a tightening ecosystem. But for a truly successful long-term bet, these startups need to be helmed by a founder who understands running a startup beyond the lab. 

That understanding can be developed through a willingness to learn, but the willingness needs to be there from the beginning. Here are some essential traits and technical know-how: 

Regulatory mastery 

Because FDA and CE clearance is routinely mistaken for market readiness, founders need the discipline to treat clearance as one milestone among several and to resist the false confidence that a cleared product sells itself. This is a form of intellectual honesty specific to health. Founders need to know the difference between being legally allowed to sell a product and building something someone will buy.

Given how differently HIPAA and GDPR treat health data, founders with any international ambition need to think architecturally about compliance from the beginning, deciding early whether they're building one global system or several regional ones. This is an unusually senior, almost CTO-plus-general-counsel form of judgment to expect from an early founder.

Holding technical conviction and commercial humility simultaneously

The same scientific training that makes someone rigorous about uncertainty can undercut them in investor and hospital-buyer conversations that expect confidence. Health founders need to switch registers depending on their audience. They need to be precise and hedged with clinicians and regulators, confident and decisive with investors and procurement committees. These modes shouldn’t compromise their judgment about what's actually true either.

Equally an openness to learn from mentors in different sectors, who understand the rigmarole of growing a business is an essential trait. 

Reimbursement as a design discipline

Given that reimbursement codes and pathways gate the business as much as regulatory clearance does, the founder needs to be able to understand who pays, under what code, and why before writing a single line of product spec. This is closer to a policy/health-economics skill than an engineering one, and it's rare in scientist-founders by default.

Multi-stakeholder fluency 

Because the person who uses the product is often not the person who approves it or pays for it, a health founder needs to think and pitch in at least three languages simultaneously. They need to talk clinical value to the physician, workflow and liability value to the hospital, and cost/outcomes value to the payer. This is a genuinely different skill from the single-buyer sales logic most software and even other deep-tech founders rely on.

Building a team that wins on all fronts

Investors are in equal parts interested in the product and the team. And ultimately it's the team who decides the product’s long term success. Bringing in experienced people from reputable backgrounds sends signals that this is a serious business with the right people to take it from 0 to 1.

"When the disease you are working on is still being characterised, you can't borrow a validation playbook. It takes real rigor about what your evidence proves, and equal flexibility to keep redesigning the protocol as the science shifts."

Raphaelle Taub Co-founder of Matricis.ai

Commercialisation pathways

Unlike most deep-tech, health founders don't walk a single commercialisation road. The pathway depends entirely on whether they're building a drug, a device or software. As well as what territories they plan to build in, each coming with their own regulatory pathway:

Track 1: Therapeutics (drugs, biologics)

This is the longest and most capital-intensive track. The commercialisation pathway runs through preclinical research, clinical trials, regulatory submission and review, manufacturing scale-up, and market launch. In practice this looks like:

• In the US, the pathway runs through five phases: preclinical research (1–3 years), clinical trials Phase 1–3 (6–7 years, with Phase II as the key jump-off point), FDA regulatory submission (6–12 months), and manufacturing scale-up and launch, with only about 10% of drugs entering trials reaching commercialisation and total cost ranging from $314 million to $2.8 billion.

• In the EU, the equivalent route is EMA's Centralised Procedure, granting a single marketing authorisation valid across all member states; the UK, having lost automatic access to that route post-Brexit, now runs its own national licensing system alongside the International Recognition Procedure (IRP), which lets a UK marketing authorisation be granted in as little as 60 days by relying on an existing FDA or EMA approval, making the UK often the fastest of the three once a drug already has US or EU clearance.

The odds and cost are sobering. In the US only about 10% of drugs entering clinical trials reach commercialisation and the full journey costs anywhere from $314 million to $2.8 billion. The lesson for a founder is to budget and plan milestones around this reality from day one, since most life sciences companies do make it through preclinical trials, but waste significant money doing so if they don't understand the regulatory complexity going in.

Track 2: Medical devices (including AI-enabled/robotic devices)

Device founders need to decide early which regulatory pathway fits the product.

• In the US, the FDA offers three device pathways scaled to risk and novelty: 510(k) for moderate-risk devices with an existing predicate (used for roughly 99% of the 155,000+ devices cleared since 1976), De Novo for novel low-to-moderate-risk devices with no predicate, increasingly the route for first-of-their-kind AI products, and PMA for high-risk Class III devices requiring the most extensive evidence. Choosing the wrong route can result in 8–12 months of added delay, and total software-device approval costs range from $50,000 to over $5 million depending on pathway and clinical trial requirements.

• The EU runs on self-declared conformity backed by independent Notified Body oversight rather than a single government decision. 

  • Devices are classed I, IIa, IIb, or III

  • Review timelines of 3–6 months for Class I but 8–36 months for higher classes, driven largely by Notified Body capacity constraints

  • AI-enabled devices face a second, parallel compliance track under the EU AI Act on top of MDR. 

  • The UK now runs its own UKCA system alongside a transitional acceptance of CE marking, and its 2026 International Reliance Pathway lets devices already approved in Australia, Canada, or the US get accelerated UK access.

Track 3: Digital health / AI software (Software as a Medical Device, SaMD)

This is the newest and fastest-evolving track, and the one most relevant to AI-driven health startups.

• In the US, the first step is correctly classifying whether a product qualifies as SaMD at all before choosing between 510(k), De Novo, or PMA, with regulators now developing Predetermined Change Control Plans (PCCPs) to govern AI models that keep learning post-launch. 

• The EU layers its own AI Act on top of MDR for any AI SaMD classified as high-risk, creating two parallel compliance obligations rather than one, while the UK's 2026 regulatory update introduces its own mandatory PCCP and cybersecurity requirements for SaMD under UKCA. Meaning founders building AI-driven health software for multiple markets are effectively managing three separately evolving software-specific regimes, not a single global standard.

Start planning for commercialisation as early as the preclinical or pre-launch phase. Reimbursement strategy, production costs and competitive landscape will all need answers well before regulatory submission. 

Increasingly, investors want to know whether a founder understands the company's full path to commercialisation and whether the milestone plan is realistic for the requested funding.

The barriers to health deep-tech are coming down

The product might be the breakthrough but choosing the path and the people to bring a deep-tech startup in health forward will decide the venture’s success. 

What separates the founders who make it through fifteen years of trials, notified bodies, and payer negotiations from those who stall isn't usually the underlying science. By the time a company reaches Series A, the technology has typically already been validated in some form. What decides the outcome is whether the founder built the commercial and regulatory framework around that science early enough and whether they assembled a team that could hold clinical rigour, regulatory judgment, and payer fluency at the same time.

The ecosystem is making that job more achievable than it was even five years ago. Grant funding has widened, corporate and hospital partnerships are more willing to open their data and clinical infrastructure to outside founders, compressing the distance between an academic finding and a fundable, de-risked company. Institutions that once treated commercial partners as an afterthought are now co-designing pathways to get promising science into patients' hands faster. That’s precisely because they recognise that clinical excellence and commercial execution have to be built together.

Founders in deep-tech health will be defined by who understood earliest that the science and the system it has to move through to commercialisation are the same problem.