For decades, scientific discovery has been constrained in part by the time and expertise required to develop and prioritize hypotheses. AI is beginning to change that equation. 

As Samantha Paoletti framed it, AI can potentially propose enormous numbers of molecules, targets and experimental designs. But those ideas still need to be tested in the physical world. The bottleneck may therefore be shifting from generating hypotheses to executing experiments. 

Much of the underlying automation technology already exists. The bigger challenge is that it often exists in isolation. As Christoph Jung described it: 

“The way we built things was in islands of automation.”  

Individual workflows may be highly automated, but connecting them into a coherent research environment remains difficult. Throughout the discussion, connectivity and data quality emerged as two particularly important barriers. 

Different instruments have traditionally been built around proprietary software, interfaces and protocols, making connectivity one of the most immediate challenges facing the lab of the future. 

Jung acknowledged that vendors themselves have contributed to this fragmentation through proprietary systems and argued that the industry now needs to move towards more open APIs and interfaces. The longer-term goal is to make automation accessible not only to specialist engineers, but directly to the biologists and chemists conducting the science. 

Robert Zechlin similarly emphasized the importance of common standards. With thousands of laboratory devices in use across the industry, individually building and maintaining integrations is difficult to scale.

“The only way we get to autonomous labs, to self-driving labs, is if the vendors actually adopt standards.”

Other industries offer a useful lesson. In automotive manufacturing, interoperability accelerated when customers demanded common connectivity standards from suppliers. A similar approach could help laboratory automation move from bespoke integrations towards infrastructure that can be deployed more easily across organizations.

The ultimate vision is compelling: AI proposes an experiment, automated systems execute it, the results are analyzed, and those findings inform the next experiment – creating a continuous learning cycle with limited human intervention.

Parts of this closed-loop experimentation model are already feasible. Jung pointed to bounded workflows such as protein or DNA purification, while stressing that scaling the concept across entire laboratories is much more complex. 

“When you talk about autonomous labs and the closed-loop workflow over a couple of labs, I think that is still in the very early stage.”  

Practical issues quickly emerge. How are reagents and consumables replenished during experiments running for days? How does a system recover from an unexpected equipment error? And how does it distinguish an error from a scientifically meaningful result? 

The distinction between closed-loop and fully autonomous laboratories is therefore important. Progress is more likely to happen workflow by workflow, with systems gradually taking responsibility for clearly defined decisions.

For Zechlin, this also means focusing on making today’s automation reliable and useful before becoming too distracted by the promise of fully autonomous labs. Many existing work cells still operate well below their potential, making reliability, connectivity and return on investment immediate priorities. 

“Let’s get to stuff that runs first.” 

Rather than treating AI-driven autonomy as the starting point, the discussion pointed towards a more pragmatic progression: build reliable automation, connect systems and data, and then layer increasingly intelligent capabilities on top. 

For organizations to hand greater control to AI-driven systems, technical capability alone will not be enough. As Cutler said:

“Trust is something that… is earned.”

That trust will need to be built through controlled experiments, reproducibility and clear boundaries. An autonomous system must not only perform an experiment successfully, but recognize when something has gone wrong, determine whether it can recover safely and know when human expertise is required.

This suggests a changing role for scientists rather than their removal from the process. Humans are likely to remain responsible for setting objectives and guardrails, validating outcomes and intervening when systems encounter unexpected situations.

Science also presents another challenge: serendipity. Unexpected findings can be enormously valuable. Teaching systems to recognize when an unexpected result may be scientifically interesting, rather than simply an error, will be an important frontier.

If AI is to make increasingly sophisticated experimental decisions, the quality and context of the data feeding those decisions becomes fundamental. 

Simply producing more data is not enough. Results need context: temperatures, timings, calibration data, instrument status, reruns, errors and potentially sensor or camera feeds. As Cutler put it:

“You can have a ton of data, but if it’s not good enough data, it’s not going to feed the model.”

This is particularly significant for organizations with decades of historical research data. Volume alone does not make that information AI-ready. Without sufficient annotation, metadata and quality controls, potentially valuable datasets can be difficult to reuse effectively. 

Jung similarly emphasized the need for contextualized data that allows a system to assess not only a result, but the conditions under which it was produced. For organizations, this makes data governance part of the scientific infrastructure required for AI-enabled research. 

Despite the focus on AI and robotics, the discussion repeatedly returned to one distinctly human challenge: adoption. 

Connected, automated laboratories require investment, new skills, governance and changes to established ways of working. In large organizations, fragmented ownership of automation, software and informatics can make this particularly difficult.

Cutler argued that greater centralization and a willingness to invest and take risks will be important.

“You can’t implement things piecemeal across 20 different places and then coordinate it.” 

Smaller companies may have an advantage. Their ability to make decisions quickly, establish common infrastructure from the outset and experiment with new operating models could allow them to demonstrate approaches that larger organizations subsequently adopt. 

Technology adoption also requires people to understand its purpose. As Jung emphasized, organizations need to communicate not only the risks of new technologies, but the opportunities and benefits they create. 

So, what will the laboratory look like five or ten years from now? 

The panel offered different perspectives on the speed of change, but there was broad agreement on the direction: laboratories will become increasingly connected, automated and AI-enabled. 

We are likely to see more closed-loop workflows, greater orchestration between instruments and software, and scientists moving away from repetitive experimental execution towards supervision, interpretation and decision-making.

But the pace of technological development may not ultimately determine how quickly this future arrives. As Cutler observed:

“The technology is going to be there way before we are.”

Standards, regulation, cybersecurity, governance and organizational readiness will all influence how quickly new capabilities can move from experimentation to widespread adoption. 

Ultimately, the lab of the future will not be created by AI, robotics or automation alone. It will emerge at the intersection of these technologies – bioconvergence in practice – and through collaboration between scientists, technology providers, pharma companies, startups and regulators.

The technology is moving quickly. The challenge now is ensuring that the infrastructure, organizations and people around it can move with it. 

Watch the full panel discussion on the Basel Area Business & Innovation YouTube channel to hear the experts explore the future of labs.  

Many organizations are already experimenting with AI. That is not the issue.

The issue is that too many of these experiments sit in isolation. Individual teams launch promising pilots. Technical groups test new tools. Business units look for quick wins. But the initiatives often remain disconnected from each other and from the strategic outcomes that matter most.

Petra described this as the “thousand flowers bloom” stage of AI adoption. It creates activity, but not necessarily impact.

For pharma and healthtech leaders, a pivotal question is “Which part of the value chain should we start to reinvent?”

That shift matters because clinical development is not held back by one single bottleneck. Delays come from handoffs, rework, fragmented data, sequential decision-making and limited visibility across the full development lifecycle. Automating one task can help. Connecting the system can change the performance of the whole process.

Petra made a sharp distinction between optimization and reinvention. 

Optimization improves the current process. Reinvention asks whether the process would look the same if it were designed today with AI, data and digital orchestration built in from the start. 

In clinical R&D, that distinction is critical. Industry pressure is rising through patent expirations, productivity challenges and more complex trials. Incremental improvements alone are unlikely to close the gap. 

For Petra, the answer sits across three dimensions: work, workforce and workbench. 

Work means moving from siloed tasks to end-to-end intelligent workflows. Workforce means preparing teams to work with AI agents, not just digital tools. Workbench means building the secure, interoperable digital backbone that allows people, agents, systems and data to operate together. 

Without all three, companies risk applying advanced technology to broken workflows. The result may be a faster version of the same problem. 

One of the clearest examples is the journey from IND to first in-human dose.

Today, this phase is heavily coordination driven. Feasibility, protocol design, site selection, vendor setup, contracting and activation often happen sequentially. Each handoff creates room for delay, rework and misalignment.

AI orchestration could change that pattern. Instead of waiting for one step to finish before the next begins, clinical teams could move toward parallel execution, continuous evidence feedback, adaptive protocol refinement and real-time visibility.

The benefit is not only speed. It is adaptability.

If a site drops out, patient recruitment slows, or a regulatory requirement changes, an AI-orchestrated workflow could support faster replanning and better decision-making. The system becomes less brittle because it is continuously learning and adjusting.

Petra also shared a concrete example of reinvention through AI: the idea of designing a study in one day. 

The concept brings together complementary AI capabilities. One solution supports evidence-based protocol generation. Another adds simulation, using computational models to test scenarios before a protocol is finalized. 

Together, these capabilities could create a closed protocol optimization loop: design, simulate, refine, generate, monitor against actuals and amend only when the evidence demands it. 

The potential gains are tangible. Petra pointed to examples including faster protocol development, faster implementation in systems such as EDC and eTMF, fewer amendments and scenario analyses that can be run in minutes rather than days. 

For clinical teams, this is more than a productivity story. Better upfront design can reduce downstream friction. Simulation can expose risks before patients are enrolled. Connected workflows can help teams make decisions with stronger evidence and less guesswork. 

The hard part regarding AI adoption is not just the technology.

Petra emphasized that agentic AI requires clear business alignment from the start. Business teams need to define the outcomes and decision boundaries. Technology teams need to build for real workflows, not abstract capability. Governance must clarify accountability, oversight and trust when agents support or take decisions.

Architecture also matters. Multi-agent orchestration and enterprise system integration are complex. They need to be designed deliberately, especially in regulated environments where data quality, traceability and human oversight are essential.

And then comes the practical test: will people use it?

For pharma and healthtech innovators, that may be the most important takeaway. Agentic AI will not transform clinical trials because the technology is impressive. It will transform clinical trials when it is connected to the right workflows, trusted by the right users and measured against the right outcomes. 

The next wave of clinical innovation will not be powered by pilots alone. It will come from clinical systems that learn, adapt and improve with every decision. 

Find about our upcoming Open Mics and other DayOne events by signing up to our newsletter here. 

About the expert

Dr. Petra Jantzer is Senior Managing Director and Global Client Account Partner at Accenture, where she advises CEOs and senior leaders across life sciences, medical technology and digital health. With more than 20 years of experience in consulting and life sciences, she focuses on digital transformation, growth strategy and innovation across the full life sciences value chain.  

Petra holds a Ph.D. in immunology and an honors degree in molecular biology. She is also President and Co-Founder of Advance, a cross-industry association in Switzerland focused on advancing gender equality in business, and represents Switzerland in the G20 EMPOWER initiative. 

Clinical trials are full of coordination work. Data must move between systems. Sites need to be selected and activated. Protocols, reports, queries and submissions need to be written, checked and updated. Vendors, sponsors, sites and internal teams all operate across different tools, formats and processes. 

That is why orchestration came up repeatedly. 

For Laetitia, the biggest opportunity is using AI as a “continuous intelligence layer” across clinical development. The point is not simply to automate individual tasks. It is to help medical writing, operations, regulatory and other functions work from a more connected view of the trial. 

Luca gave a concrete example from clinical data management, where teams still spend significant time aggregating data, reviewing lines of information, cross-checking against protocols and writing queries or reports. Agentic AI can break that work into specialized steps, with individual agents reviewing data, checking outputs and escalating what matters. 

The human role remains central, but it changes. 

That distinction matters. Agentic AI should not remove clinical judgment from the process. It should reduce manual burden so experts can spend more time on interpretation, quality, risk and patient impact. 

Robert pointed to one of the biggest barriers in large pharma: disconnected systems and inconsistent data. 

Clinical organizations often work across multiple platforms, vendors, standards and data structures. That creates friction for analytics, reporting and decision-making. It also limits the impact of AI, because poor inputs produce poor outputs. 

Agentic workflows could help by translating data between formats, reconciling site information, generating standardized outputs and making operational data easier to use across the organization. 

Robert described this as a strong starting point because the task is often clear: data goes in, data comes out, and quality can be checked. Human oversight remains necessary, but the workflow is easier to control than more subjective use cases. 

This is where agentic AI may create near-term value: not by replacing clinical expertise, but by cleaning up the infrastructure around it. 

Much of the agentic AI conversation focuses on large pharma. But the panel also highlighted a different opportunity for biotech and mid-sized companies. 

Large organizations have deep resources, but also deep complexity. They carry legacy systems, established processes, multiple decision layers and risk controls that make change slower. 

Smaller companies may be able to move differently. 

Rohit argued that many use cases are not yet proven at scale, which creates an opening for companies that can make decisions faster and avoid legacy complexity.

Laetitia saw the same appetite among biotech companies, especially where speed to market is critical. Smaller teams may be less constrained by existing structures and more willing to design workflows around AI from the start, provided regulatory and compliance expectations are built in properly. 

That does not mean every company should build its own technology. It means AI-native thinking could become a strategic advantage. 

The panel was direct about one recurring problem: too many AI initiatives are treated as technology projects rather than operating model changes. 

Pilots can prove that a tool works in a narrow context. But clinical trials operate across countries, functions, vendors and regulatory expectations. A proof of concept that works in one geography or workflow can become fragile when pushed into global production. 

Robert framed the main barrier clearly. Technology is no longer the limiting factor. The harder challenge is rethinking the end-to-end system. 

That requires process change, people change and technology change together. It also requires stronger value discipline. ROI cannot be an afterthought, especially when implementation and operating costs may outweigh benefits in some contexts. 

The success metrics themselves are not new: faster development, lower cost, higher quality and better data. The challenge is choosing the interventions that move those metrics meaningfully. 

Trust is not a soft issue in clinical development. It is a precondition for adoption. 

Internal teams need to trust that AI makes their work better, not just faster. Sites and clinicians need reliable information. Patients need safety, clarity and confidence. Regulators need traceability, control and accountability. 

Rohit emphasized that human oversight remains essential, especially in regulated contexts. The key is not whether a human stays in the loop, but how the process is redesigned so that human review is efficient, meaningful and focused on the right decisions. 

Robert added that trust will build progressively. AI systems will need close monitoring, quality checks and feedback from users. In some areas, AI may eventually make fewer mistakes than humans. But clinical development cannot assume that point has already arrived everywhere. 

The panel also touched on a strategic question facing every pharma and biotech leader: what should companies build themselves, and what should they buy or partner for? 

There was no single answer. Different companies will make different choices based on maturity, ambition, internal capabilities and competitive priorities. 

Luca put the principle simply: pharma companies are in the business of bringing drugs to patients. If proven technology already exists, there needs to be a strong reason to build it internally. That reason may be competitive advantages, unique data access or a highly specific need. 

Robert argued for a robust internal team, not to build everything, but to ensure the technology ecosystem works. Companies need governance, integration capability and enough internal control to combine external tools safely and strategically. 

Laetitia was even more direct: the question should always come back to return on investment and the patient. If agentic AI can make trials more efficient, quicker and more cost-effective, that is where attention should go. 

Agentic AI could help clinical teams move from fragmented work to connected, adaptive execution. It could improve data management, accelerate writing workflows, support study planning, strengthen feasibility and help sponsors respond faster as trials change. 

But technology alone will not do that work. The next phase depends on clinical organizations becoming more willing to redesign processes, upskill teams, connect systems and measure value from day one. It also depends on keeping the human role where it matters most: judgment, oversight, interpretation and trust. 

The opportunity is real. The advantage will go to teams that stop asking where AI can be inserted and start asking how clinical development should work now that AI agents are part of the workforce. 

Watch the full panel discussion on the Basel Area Business & Innovation YouTube channel to hear the experts explore where Agentic AI can create real value in clinical development, and what pharma, biotech and healthtech teams need to get right before it scales.

About the experts 

Laetitia Soudanas is Senior Director Clinical Solutions at Biorce, where she focuses on strategic pharma partnerships and AI transformation in clinical development. She has more than 15 years of experience driving growth in clinical and pharmaceutical settings, with a current focus on making clinical trials more efficient, trusted, explainable and impactful through AI. 

Dr. Rohit Pushparajan is Managing Director, Lifesciences R&D Lead EMEA at Accenture. A physician by training, he has more than 20 years of experience in pharma R&D and deep expertise in helping life sciences organizations rethink how R&D can become faster, more productive and more patient focused. 

Robert McGregor is Executive Director, AI Program Head at Novartis Development, where he works at the intersection of AI, technology and drug development. He describes his focus as developing and deploying novel, value-adding solutions, with a particular emphasis on innovation ecosystems and practical AI adoption in large organizations. 

Luca Finelli is VP and Global Head of Digital Endpoints and Patient Centered Solutions at Roche Pharma Product Development. His background spans digital endpoints, data science, analytics, AI and patient-centered evidence generation, with earlier roles in data and digital strategy at Novartis and research experience at the Salk Institute. 

On 7 May 2026, DayOne hosted a “Ask Me Anything” webinar to answer your most pressing questions regarding the DayOne Accelerator program, including what the program entails, which ventures are in scope and how the selection process works. 

In case you missed it, please find the top questions and answers below. 

Q1: What types of startups are eligible for the program? How many get selected? 

A: The program supports startups developing tech solutions for pharma companies across areas such as software platforms for drug discovery, clinical trials optimization, data collection, digital or imaging biomarkers, remote patient monitoring in decentralized clinical trials, and companion diagnostics for patient identification and stratification. The main criterion is whether the solution addresses a meaningful pharma R&D challenge or fits into pharma R&D workflows and operations. 

Companies working on enabling technologies, patient coordination or pharma-facing platforms may be eligible depending on their use case and value proposition in the context of pharma R&D. 

Among all applications submitted, 15 startups will get selected to join the 2026 cohort. 

Q2: What stage should startups be at to apply? 

A: The program is designed primarily for early-stage startups (pre-Seed to pre-Series A). Each year, applicants successfully selected for the cohort are a mix of companies that range from university spin-offs with strong scientific validation to more mature ventures with pilots, partnerships, customers or revenue. Startups already collaborating with pharma companies are often a particularly strong fit, especially if they are looking to refine their strategy, positioning, commercialization approach, or scale beyond initial pilots. 

Both incorporated startups and non-incorporated spin-offs are eligible, as long as they have enough operational runway and team bandwidth to actively participate in the program. 

Q3: Is there specific focus/theme or are there funding requirements? 

A: The program does not focus on specific therapeutic areas and does not require a particular funding background or institutional support. Instead, it looks for startups solving relevant challenges for pharma companies in R&D, across different technologies and domains. 

The program is generally best suited for companies that still benefit from mentoring, ecosystem access and early-stage investor exposure, rather than later-stage ventures focused primarily on fundraising or business development. 

Q4: What activities and topics are included in the program? 

A: The program combines three main elements: personalized mentoring, industry insights sessions, and networking opportunities.  

The program includes two mandatory in-person events in Basel, Switzerland, to kick off the acceleration (Kickoff Bootcamp on 7-8 September 2026) and close the program (Innovation Showcase in early December 2026). 

Each startup gets paired with a dedicated mentor to receive 1:1 support primarily to help them shape or refine their value proposition to pharma companies, and can also cover other topics such as strategy, business model development, product positioning, etc. 

The program also includes webinars, founder exchanges and sessions with pharma professionals covering topics such as: 

Topics above are illustrative only, as the program is specifically tailored each year to match the major needs of each annual cohort. 

In addition, startups gain access to multiple networking opportunities and the chance to connect with pharma stakeholders, investors, expand industry networks, and build meaningful relationships within the Basel life sciences ecosystem. 

To support startups’ participation in the in-person events (Kickoff Bootcamp and Innovation Showcase) held in Basel during the program period, DayOne provides a travel grant for each team.

Travel grant amount depends on the country or region: 

Note: the program does not provide direct “warm introductions” or mass contact lists. Instead, startups are expected to actively engage with the network opportunities provided. 

Q5: Does the program help startups find pilots with pharma companies? 

A: The program helps startups refine their strategy, positioning and understanding of how to work effectively with pharma companies.  

The program does not directly arrange pilot projects or perform matchmaking. That said, startups do pitch to pharma executives during the program, which can naturally lead to further discussions if there is strong alignment. 

Q6: How intensive is the program, and who should participate? 

A: The program includes 1:1 mentoring sessions, expert interactive sessions, kick-off bootcamp, closing innovation showcase and networking opportunities, but the overall intensity depends on how actively each startup engages. Teams that invest time, actively contribute to sessions, and apply the feedback received tend to gain the most value. 

The program is designed to work well even for small teams, including startups with only 3–4 people. At least one senior team member should participate consistently throughout the program, while additional team members can join sessions relevant to their expertise, such as sales, product, regulatory or technical topics. 

Q7: How does the tailored mentoring work? 

A: Each of the 15 startups from the cohort gets matched with a mentor for the duration of the program. Matchmaking is done by the DayOne team and takes into consideration both startups and mentors’ preferences. Mentors and startups are carefully paired based on the startup’s stage, goals and specific needs, with expertise ranging from technical and regulatory topics to medical and commercialization. 

Startups typically schedule one mentoring session per week, totalling around 12–15 hours of mentoring throughout the program. The mentoring format and pace are flexible and mutually agreed between the startup and their mentor, ranging from short weekly check-ins to deeper strategy sessions.  

Upon discussion with the program manager, if similar needs arise from the cohort, additional cohort expert sessions can also be added during the program. 

Q8: Does the program arrange NDAs between mentors and startups? 

A: Not directly. DayOne’s contract with mentors includes a confidentiality clause. However, startups may independently establish NDAs with their mentor, pharma companies, investors, experts or partners involved in the program if needed. DayOne signs an NDA with participating ventures during DayOne NEXT, the hands-on post-acceleration phase (open to up to 3 startups following the initial accelerator program). However, because the program involves multiple startups sharing common spaces and activities, startups are encouraged not to disclose highly confidential information during open sessions. 

Q9: Is AI used in the selection process? 

A: AI may be used internally to support administrative tasks such as summarizing applications or improving review processes, but all applications are pre-screened by a human, then reviewed in-depth by human reviewers and Selection Committee members. Selection decisions are not made by AI. 

Q10: How are startups selected for the post-accelerator program, DayOne NEXT? 

A: Selection is based on each startup’s performance and engagement throughout the 3-month program, including progress made, responsiveness to feedback, mentor and industry evaluations, and overall growth potential. The program also considers whether establishing a presence in Basel aligns strategically with the company’s stage and goals. To unlock the non-dilutive cash grant (up to 50,000 CHF) and further funding (such as R&D incentives from the Basel-Stadt canton), some R&D activities should be performed in Basel. 

We hope this answers your questions and you are ready to apply to the Accelerator. You can also check out the FAQ here. The deadline is 31 May 2026. Apply here.  

We are excited to announce that Lemna Bio, a standout company from the DayOne Accelerator 2025 cohort, is the first startup selected to join DayOne NEXT, our long-term support program for the most promising DayOne founders. 

DayOne NEXT reflects a simple belief: the journey doesn’t end with the accelerator – it’s just the beginning. Designed as a hands-on, long-term venture builder, the program supports a small number of exceptional startups as they build, scale and anchor their R&D in the Basel Area. Participants receive a non-dilutive CHF 50,000 grant, up to 12 months of tailored operational and strategic support (valued at up to CHF 50,000), workspace at the Switzerland Innovation Park Basel Area, and deep integration into the region’s world-leading life sciences ecosystem.  

By working side-by-side with founders, DayOne NEXT provides not just resources, but sustained, highly personalized support – from fractional expertise and mentorship to ecosystem access and visibility opportunities – helping startups transition from early traction to long-term impact.  

Lemna Bio is a techbio company building a structural AI platform to decode and validate hard intracellular targets. While advances in chemistry are unlocking new ways to engage biological targets, failures in drug discovery are still largely driven by incorrect target selection or an incomplete understanding of how to engage them in a disease context. Lemna Bio develops the tools, intelligence and experimental data to help drug discovery teams de-risk target biology and disease mechanisms early – before committing to costly chemistry efforts. 

Commenting on the milestone, Moustafa Houmani, Co-founder & CEO of Lemna Bio, said, “DayOne provided us with rare, first-hand insights into how pharmaceutical companies operate – insights that typically take years to acquire. Being selected for DayOne NEXT is an exciting opportunity to continue building with the right support and ecosystem around us.”  

With Lemna Bio as the first company in DayOne NEXT, we are proud to deepen our commitment to the most promising founders in our community and to support them as they scale breakthrough innovations at the intersection of technology and pharma R&D. 

In March, the global healthtech community gathered in Basel for the inaugural health.tech global summit, bringing together leaders from across healthcare, technology, investment and policy. Over three days, the summit positioned itself as a new international meeting point for the rapidly evolving healthtech ecosystem. 

With around 6,000 participants from more than 60 countries, the event brought together startups, pharma companies, hospital leaders, policymakers and investors to explore how technology is reshaping healthcare. 

As a proud partner of the event, DayOne played an active role throughout the event – powering the startup competition, supporting the investor speed dating and hosting a booth on the exhibition floor. DayOne also led several panel discussions on relevant topics, including early-stage healthtech fundamentals, Basel as a healthtech hub, and investment in tech-enabled pharma R&D.

Hosted in one of the world’s most important life-science hubs, the summit leveraged Basel’s unique ecosystem – home to major pharmaceutical companies, research institutions and health startups. 

The goal of the event was clear: create a cross-industry platform where healthcare and technology leaders can collaborate to solve some of the most pressing challenges in medicine. 

Over the course of the summit, attendees engaged in: 

More than 200 exhibitors and more than 150 startups participated, while hundreds of investors explored emerging healthtech opportunities. 

A recurring theme across the conference was the growing role of artificial intelligence as core healthcare infrastructure rather than an experimental tool. 

Discussions highlighted how AI is already transforming multiple parts of the healthcare value chain, including: 

Speakers emphasized that the next major challenge is not building AI tools but integrating them safely into real clinical workflows. 

As healthcare becomes more digital, conversations at the summit also focused heavily on data governance and regulatory frameworks. 

Experts debated how policymakers and innovators can balance innovation with patient safety, privacy and transparency. With new regulatory initiatives emerging globally, many sessions examined how healthcare systems can build trusted digital infrastructures that enable responsible data sharing. 

The consensus: interoperable data systems will be essential for unlocking the full potential of AI in healthcare. 

Another major theme was the shift toward predictive and preventive healthcare. Advances in digital health tools – such as wearable devices, remote monitoring technologies and AI-driven risk modeling – are enabling healthcare providers to move beyond treating illness to identifying health risks earlier and intervening sooner.  

This shift could fundamentally reshape healthcare systems by improving outcomes while reducing long-term costs. 

The summit also served as a major platform for healthtech startups and investors. 

More than 150 startups, including many DayOne alumni startups, showcased innovations ranging from AI diagnostics to digital therapeutics and healthcare infrastructure software. The startup competition, powered by DayOne, highlighted several emerging companies, with finalists pitching their solutions to a panel of global investors and industry leaders. The competition concluded with PulseMedica taking home the CHF 10,000 cash prize from University Hospital Basel. 

DayOne also supported the investor speed dating, including short, focused four-minute conversations with fast switching between participants. 

Investor interest was particularly strong in areas such as AI-powered diagnostics, remote patient monitoring, digital therapeutics and healthcare data platforms. The event reinforced the sense that healthtech remains one of the most active areas for venture investment in the life sciences sector.  

The summit featured a diverse range of voices from medicine, science, technology and global policy. Among the notable speakers was Anthony Fauci, who shared insights on global health challenges and the future of biomedical innovation. 

Former astronaut Tim Peake delivered a session on innovation, human performance and the future of health in space and on Earth, highlighting how research conducted aboard the International Space Station is driving advances in human performance monitoring and healthcare technologies on Earth. 

In another session, Amal Clooney explored how AI can serve as a powerful equaliser – expanding access to justice, protecting human rights and empowering women worldwide. 

Other sessions examined topics such as the ethics of AI in healthcare, digital transformation in hospitals and the future of pharmaceutical research. 

The debut of the health.tech global summit demonstrated strong momentum for a new international platform dedicated to the intersection of healthcare and technology. 

More importantly, the conversations in Basel highlighted a clear transition for the industry: digital health is moving beyond experimentation and toward large-scale implementation. As AI, data platforms and connected health tools mature, the next challenge will be turning innovation into real-world impact – improving care delivery, expanding access and ultimately building more resilient healthcare systems. 

If the energy and collaboration seen in Basel are any indication, the future of healthtech is already taking shape. 

The dates are set for health.tech | global summit 2027. Find out more details here. 

Drug discovery rarely moves at the speed of human needs. Between ballooning trial costs, brittle data pipelines and the sheer biological complexity of diseases, pharmaceutical R&D has long been a slow, expensive and high-risk endeavor. Yet over the past few months, a new cohort of healthtech startups has been quietly working on changing that – not on their own, but together inside a 3-month hybrid healthtech accelerator program in the heart of Basel’s life sciences innovation hub.

In 2025, 14 Swiss and international startups completed the DayOne Accelerator program.  As the program’s culmination at the Innovation Showcase in December made clear, the breakthrough wasn’t just the technology on display, but the shared conviction that collaboration can compress timelines that are currently still stretching over a decade.

The testimonials below illustrate how these startups are reshaping what’s possible in pharma R&D – and how accelerator programs like DayOne can play a critical role in supporting and speeding up their growth and impact. Together, these perspectives point to a broader transformation underway across the pharma industry: a move away from fragmented, linear models of research toward faster, more interconnected and more collaborative ways of working. 

“The DayOne accelerator was an excellent experience that significantly accelerated our entry into the Basel life sciences ecosystem. It provided us with rare, first-hand insights into how pharmaceutical companies operate – insights that typically take years to acquire,” shared Moustafa Houmani, Co-founder and CEO of Lemna Bio, a startup building AI models that explain how molecules interact, from atomic-level interfaces to biological networks. “I would strongly recommend DayOne to startups that are serious about building the next generation of drug discovery companies. Importantly, the program is both fee-free and equity-free.” 

“DayOne is a fantastic accelerator for any company operating at the intersection of tech and biopharma R&D. The DayOne accelerator provided us with high-quality mentorship, as well as connections with a very large network of executives and pharma experts,” said Nives Rombini, Co-founder and CEO of Navis Bio, a startup up developing AI and quantitative methods to analyze scientific and clinical evidence supporting biopharma strategic decisions. “DayOne is also a very well-known brand in the Basel pharma ecosystem which was of great help for us to expand the presence of Navis Bio in Basel.” 

“Joining the DayOne program has been one of the best decisions for our startup. The program offers truly hands-on, founder-focused support, with deep industry insight and a strong ecosystem. The coaching, mentorship and network helped us sharpen our strategy and move forward with much greater clarity and confidence,” noted Yanan Zhang, Co-founder of ZuriEV, a startup developing a blood-based liquid biopsy test enabling earlier, non-invasive brain cancer diagnosis. “DayOne feels less like an accelerator and more like a long-term partner in our journey.” 

“DayOne has been a great partner for Octozi to build relationships within the Swiss pharma ecosystem. Their understanding of the local life sciences landscape and ability to connect us with the right sponsors and stakeholders has meaningfully accelerated our progress,” shared Amit Patel, Co-founder and CEO of Octozi, a startup developing an AI platform that helps biopharma sponsors automate clinical development processes for data cleaning and review. “DayOne has played a key role in helping us establish credibility and long-term partnerships in Switzerland.” 

“DayOne is the premier hub for any startup serious about bridging the gap with the pharmaceutical industry,” said Edoardo Giudice, Co-founder of Virtuosis AI, a startup developing AI models analyzing vocal biomarkers for NCS, cardiometabolic and respiratory disorders. 

“An incredibly impactful experience, highly practical, thoughtfully structured, and supported by exceptional mentors and experts,” noted Anna Cichonska, Co-founder and COO of Harmonic Discovery, a startup developing a machine learning-driven drug discovery platform designing multi-target kinase therapeutics with precision pharmacology. “The DayOne team and the startup cohort created an environment that made learning and progress feel both focused and collaborative.” 

“The DayOne programme has been a real accelerator for us, helping pave the way for partnerships with pharma. It allowed us to fast-track our understanding of the ecosystem, the various stakeholders, their roles and challenges, and enabled us to fine-tune our value proposition. The program also created unique opportunities for us to meet and engage with people at the heart of the Basel life sciences and tech ecosystem,” shared Lea Pignier, Co-founder and CEO of Areltys, a startup developing a digital platform connecting cancer patients to innovative treatments, especially radioligand therapies worldwide. “The DayOne team, along with the experts they bring together, have immense experience, a keen understanding of startup challenges, and a genuine commitment to supporting startups and founders. The DayOne programme is truly unique, full of energy and has been extremely valuable for us.” 

“DayOne provided great value through their mentorship and the doors they opened for us, as well as all the insights we could gather from top pharma executives,” said Antoine Herzog, Co-founder and CEO of Parithera, a startup developing a next-generation CTC liquid biopsy platform to detect treatment resistance and guide precision oncology. 

“I would highly recommend the DayOne Accelerator for any healthtech startup. The insights, expert access and real-world feedback were incredibly valuable. If you’re building in this space, this is where you want to be,” noted Anna-Liisa Parts, Co-founder and COO of Menken Trials, a startup developing an AI-enabled clinical trial platform automating monitoring, compliance checks and essential document management. 

Applications for the 2026 edition of the DayOne Accelerator open at the end of March. If you’re building a venture at the intersection of technology and pharma R&D – or know someone who is – stay tuned for more information on how to apply, what we look for and what to expect from the program. More details coming soon! 

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A few years ago, “doing omics” often meant generating a single layer of insight and handing it off downstream. The conversation made clear that pharma has moved on: multi-omics now matters most when it connects to validation and development decisions, not when it produces isolated signals.

The implication for innovators is straightforward: the value proposition is increasingly platform oriented. The winning solutions reduce uncertainty at a specific decision point (or across several), and they come with a realistic path to operationalization inside a drug development organization.

The panel converged on a pragmatic definition of value: multi-omics earns its keep where it is already close to routine use and improves decisions across the chain.

Three “value zones” surfaced clearly:

Target identification and prioritization, with a validation plan attached

Integrated datasets – including preclinical, patient and real-world sources – help teams move beyond the “crowding problem” in oncology (too many efforts converging on the same small set of targets). But identifying more targets is not the win; the win is identifying targets that can be validated and developed with a credible downstream plan.

Molecule optimization using functional readouts

Modern biologics are increasingly complex, and teams need functional readouts to guide iteration. Omics-derived signals can provide that functional feedback loop, especially when combined with robust experimental workflows.

Clinical development and trial design

Longitudinal, multi-modal data collected in trials (not only bulk RNA sequencing) enables a more granular understanding of patient populations – supporting better trial design, patient stratification, and next-generation product criteria.

A recurring reality check is that multi-omics models can be scientifically impressive and still fail the “clinical feasibility” test. If your biomarker requires a long list of assays, modalities and feature-heavy models, clinicians may simply be unable (or unwilling) to use it in real-world trial design.

This is not anti-innovation – it is the translation bottleneck in plain terms. The path forward discussed on stage was not “less ambition,” but better constraint handling: interpretability, feature reduction that respects clinical logic, and smarter proxies for expensive measurements.

One concrete direction highlighted was predicting richer omics layers from more readily available clinical inputs (for example, extracting spatial or proteomic signals from standard pathology slides). Not “there yet,” but viewed as a strategically important route to speed and adoption.

If there was one word that kept resurfacing, it was validation.

In discovery, “ultimate validation” (a drug succeeding in the clinic) may be a decade away. In trials, validation may require embedding new measurements or endpoints into prospective designs, which introduces friction: incentives, timelines, regulatory comfort, and infrastructure maturity.

In clinical settings, the bar becomes existential: decision support impacts patient outcomes, and the expectation shifts toward rigorous evaluation (including trial-grade validation) rather than retrospective benchmarks.

For startups, the practical takeaway is to define validation in a staged way: what “good” looks like now, what must be true for adoption, and what prospective evidence is needed to earn expanded scope.

Even the best model fails if no one uses it. Trust, the panel argued, is not a slogan – it is built through usability, transparency, internal dogfooding, and human-in-the-loop workflows that reveal failure modes early.

One particularly sharp nuance: “explainability” is not the same as forcing AI to mimic human reasoning. If a model outputs a story that sounds reasonable but does not reflect how it produced the prediction, that can create false confidence rather than real trust.

The discussion treated partnering as a strategic reality – but also a survival question for innovators: can you maintain differentiation as methods and tooling evolve quickly?

A key point from the pharma side: technical models alone are not enough to differentiate long-term. The stronger partnering case often combines:

• unique data access (or ability to generate new data),
• operational speed and execution capability,
• and a clearly scoped measurable outcome tied to drug development decisions.

From the startup perspective, partnering can also be a rational risk allocation strategy for pharma teams facing a fast-moving methods landscape, where internal reinvention every six months is neither practical nor desirable.

If you are building a multi-omics, data science, or AI-enabled solution that can help pharma R&D make better decisions – from discovery through clinical development – DayOne is here to help you accelerate the path from promise to platform. 

Explore DayOne’s startup support (e.g. the DayOne Accelerator), sign up for our newsletter to be informed about our upcoming Open Mic sessions, or get in touch to discuss how we can support your journey from early validation to scaled partnerships. 

Please note: This article was based on discussion during the recent Open Mic: Next in Health event held on February 09, 2026, titled “Multi-omics intelligence: Platforms accelerating precision drug development”.