Precision medicine has long been a strategic focus in healthcare, but its potential is being redefined by new tools, data and approaches. Moving beyond one-size-fits-all treatment models, the field now leverages highly tailored, data-driven methods for diagnosis, prevention and therapy.
Biomarker innovation is a key component of this transformation. While biomarkers have been part of clinical practice for decades, recent advances in how they are discovered, measured and applied are offering deeper insights into disease mechanisms, allowing better prediction of disease progression and individual variability, thereby improving prediction of therapeutic response. These advances are helping researchers and clinicians stratify patients with greater precision and confidence.
As innovation accelerates, however, challenges around clinical translation, regulatory trust and scalable implementation continue to delay integration into clinical practice. In biotech and pharmaceutical ecosystems like Switzerland’s Basel Area, leading companies, startups and researchers are working to bridge the gap between scientific potential and clinical reality. Scaling precision medicine effectively requires systems thinking, strategic partnerships and new models for innovation.
Defining precision medicine and its clinical promise
Precision medicine and personalized medicine are often used interchangeably, but the distinction between them reflects an important evolution in how care is delivered. Personalized medicine refers broadly to tailoring treatments to individual characteristics, while precision medicine takes this further by applying data at scale—integrating molecular, genetic, environmental and lifestyle information to predict how a patient will respond to a specific treatment.
The shift from personalized to precision medicine represents a deeper commitment to understanding the biological complexity of both disease and the individual. Precision medicine relies on patient stratification, which allows clinicians to group individuals by relevant characteristics—such as biomarkers, genetic mutations or expression patterns—in order to match them with the most effective therapy. It also opens the door to individualized dosing and adapting treatment intensity or frequency based on predicted response or risk of side effects.
Biomarkers—measurable biological indicators that can inform diagnosis, prognosis and treatment decisions—lie at the heart of this transformation. Whether used to monitor disease progression or determine drug efficacy, biomarkers are increasingly important to how clinicians deliver targeted therapies with greater confidence.
”Precision medicine is about finding the right treatment for the right patient every time. It sounds simple but obviously it isn’t. It means breaking down what was once seen as a single patient population into individuals and understanding, even within one person, how molecular genetics, lifestyle and environmental factors all influence a patient’s response to treatment.
Sarah CarlDirector of Data Science, Scailyte
This granular understanding is pushing the field beyond treatment selection into disease prevention, early detection and continuous patient monitoring—forming the foundation for a more proactive, precise and patient-centric model of care.
Innovation in precision medicine: How industry leaders are shaping the biomarker landscape
Biomarker discovery is no longer confined to academic research or traditional lab settings—it is being accelerated by industry leaders using cutting-edge technologies, agile R&D models and cross-sector collaboration. Two companies, Scailyte and Roche Diagnostics, illustrate how innovation is being redefined across the biomarker value chain.
Scailyte focuses on single-cell omics combined with Artificial Intelligence (AI) to uncover previously undetectable biomarkers. By analyzing the behavior of individual cells, their platform captures a more detailed picture of disease heterogeneity—one that can reveal subtle signals overlooked by bulk analysis. Their use of AI in biomarker discovery enables early detection and precise patient stratification by processing complex, high-dimensional data to identify patterns linked to disease states.
Roche Diagnostics is approaching innovation from another angle—investing in experimental platforms for early detection. With a focus on diseases where validated biomarkers are still lacking, such as neurodegenerative and inflammatory conditions, the company is building tools that enable faster, more sensitive detection at earlier stages. These efforts are being developed within a dedicated innovation ‘think tank’ environment to enable operations with startup-like speed and flexibility.
Digital biomarkers are also gaining traction as a complementary innovation, especially for chronic and neurological conditions. By collecting real-time physiological data via wearable devices and sensors, researchers are beginning to explore how these digital signals can augment molecular biomarkers, improve disease monitoring and support continuous, real-world assessment of patient health.
From Parkinson’s disease and inflammatory bowel disease (IBD) to endometriosis and cancer prognosis, these innovations are already being applied to high-burden, high-complexity conditions. While their technologies differ, both startups and pharma agree: the key to success lies in partnership. Whether through collaborations with academic labs, licensing IP from small ventures or co-developing validation studies, progress depends on breaking silos.
”It’s not about optimizing what is already there—it’s about creating something entirely new, pushing into areas where validated biomarkers don’t yet exist but where the clinical need is clearly urgent. That’s why we built an experimental environment within Roche, to test ideas quickly, take risks and collaborate with external partners who are pushing the science forward.
Elke GlasmacherGlobal Head of Discovery Sciences, R&D, Roche Diagnostics
Bridging the gap from scientific discovery to real-world implementation
While scientific advances in biomarker discovery and precision diagnostics are accelerating, their adoption in everyday clinical practice is lagging. Many of the most promising technologies remain stuck in the translational gap, a space where regulatory hurdles, outdated frameworks and real-world constraints slow or prevent implementation.
One of the biggest challenges lies in regulatory alignment. As precision diagnostics become more complex, integrating AI models, multi-omic data or digital biomarkers, they often do not easily fit into existing approval pathways. Furthermore, reimbursement structures and business models are still optimized for traditional, one-size-fits-all care, rather than personalized care pathways informed by companion diagnostics.
Clinical trial design is another limiting factor. Standard models often fail to capture the granularity needed to validate new biomarker tools or reflect the diversity of real-world patient populations. This disconnection between scientific potential and clinical validation creates friction for both innovators and regulators, delays benefit for patients, and continues to limit the clinical implementation of biomarkers in real-world settings.
A related concern is data transparency and trust. As AI enters the biomarker space, questions around explainability and evidence become central. Clinicians need confidence in how predictions are made, and regulators require robust validation. Companies like Scailyte are addressing this by using supervised learning approaches and explainable AI models to ensure that biomarker-driven insights can be traced and understood, not just statistically inferred.
One way to build that trust is through the earlier integration of diagnostics and companion tools. By embedding biomarker-based decision-making earlier in the care pathway, before disease is advanced or treatment options narrow, healthcare systems can generate clearer evidence of clinical value, support regulatory confidence and move toward more proactive, cost-effective care.
”We cannot afford not to use this technology. Science is moving fast, but regulatory frameworks, data access and reimbursement models haven’t kept pace. To scale precision medicine, we need to rethink how real-world data in precision medicine can drive evidence-based adoption.
Caoimhe Vallely-GilroyDirector of Strategy, DayOne
Unlocking precision medicine at scale
Scaling what is scientifically possible into what is clinically routine remains one of the greatest—and most urgent—challenges in precision medicine. As technologies advance, the question is no longer whether they work, rather whether they can work at scale. From breakthrough biomarker platforms to real-time patient monitoring, the challenge is ensuring that these innovations are equitably accessible, sustainably reimbursed and systemically adopted.
A major part of the equation lies in restructuring incentives. Current healthcare models often reward volume over value, and diagnostics are frequently undervalued in reimbursement schemes despite their central role in guiding treatment. For precision medicine to scale, payers and regulators need to recognize the economic and clinical value of early detection, patient stratification and treatment response prediction and reflect that in policy and funding frameworks.
Equitable access remains a persistent gap. Precision approaches risk deepening existing disparities if tools and technologies are only available in specialized or well-funded settings. Broader uptake will depend not only on affordability but also on system readiness: clinician training, workflow integration and digital infrastructure that can support data-driven care.
”Scaling innovation doesn’t mean waiting for perfection. Progress often comes in incremental steps—and even partial improvements can have meaningful impact, if implemented early and iteratively.
Elke GlasmacherGlobal Head of Discovery Sciences, R&D, Roche Diagnostics
The path forward requires coordinated effort across industry, academia, regulators and health systems. It calls for shared investment in biomarker validation, streamlined regulatory pathways and clearer incentives for adoption. Just as importantly, it demands a shift in mindset—from viewing diagnostics as ancillary to recognizing them as foundational to treatment success.
Please note: This article was developed from the proceedings of the Open Mic: Next in Health event held on March 24, 2025, “Innovations in precision medicine and biomarker development.” To learn more about this event series, register for our next edition, and sign up for our newsletter, check out the Open Mic page.
