Clinical diagnostics has evolved beyond just making diagnoses. As healthcare systems move toward precision health, diagnostics is becoming integral at every stage of the patient journey from screening to disease monitoring. In this Viewpoint, we explore the enhanced role diagnostics will play in the evolving era of precision health and outline what diagnostic companies must consider for lasting success.
Technological advancements and innovation have made precision health a reality, enabling the categorization (or stratification) of patients into groups most likely to respond to specific treatments based on their genetic, molecular, and/or clinical profiles. This differs from the traditional one-size-fits-all approach, offering a more nuanced understanding of diseases. Conditions once considered common are now recognized as distinct, often rarer diseases that require personalized treatments. As a result, previously untreatable conditions can now be detected, managed, and, in some cases, cured. At the most advanced level of precision medicine, therapies are tailored to the individual.
Diagnostics are essential in enabling precision health and the development of targeted therapies, as diagnostics and treatments work hand in hand to improve patient outcomes and quality of life. Ensuring an accurate diagnosis from the outset, rather than having patients undergo multiple consultations without clear answers, is crucial. Moreover, a precise understanding of disease mechanisms at the individual level allows clinicians to select the most effective treatments from the start, reducing the likelihood of unwanted side effects or low efficacy.
For healthcare providers, this means earlier, more targeted interventions, while those funding healthcare can optimize costs and maximize the impact of their investments.
To fully realize the benefits of precision health, precision diagnostics must be seamlessly integrated throughout every phase of the patient journey — from screening and diagnosis to treatment selection, ongoing monitoring, and post-treatment follow-up (see Figure 1).
Precision health depends on fast and accurate diagnostics to ensure early, personalized interventions. Screening before symptoms appear — using biomarkers, genetic indicators, or early disease signals — can reduce health risks and costs. This is especially true when tests are accessible, such as at-home options for cervical cancer. However, in screening programs, it is essential to balance predictive power and cost to ensure both willingness to pay and widespread adoption. Additionally, the programs must demonstrate adequate sensitivity and specificity. If they fall short, confirmatory diagnostics may be required, which increases complexity and costs, and can make securing payer reimbursement more challenging.
Crucially, they must also lead to actionable outcomes. Without effective treatments, screening can waste resources and cause patient distress.
Convenience and accessibility are key to widespread adoption. For example, detecting abnormal buildup of tau protein (a marker for Alzheimer’s disease) used to require invasive spinal taps. However, thanks to technological advancements in sensitivity, these proteins are now detectable in much lower concentrations and today only require a blood sample, which in turn has resulted in much broader adoption, even for frail patients where invasive testing is not an option. Further, advances in biomarker discovery and genetic mutations associated with various diseases will continue to drive more targeted diagnostics and enable new, more precise screening programs that allow for early intervention and treatment of more diseases.
Diagnostic accuracy and speed are vital regardless of the timing of diagnoses. One of the fastest-growing segments of the diagnostic testing market is point-of-care (PoC) testing. Fueled by the COVID-19 pandemic, PoC has become essential, offering rapid results in near-patient settings. However, to succeed in the modern healthcare market, PoC tests must fit seamlessly into the patient care flow, both practically and technologically.
Other technological advancements are also contributing to increased speed and accuracy of diagnostic testing, particularly for infectious diseases. While molecular diagnostics like PCR are more sensitive than immunoassay-based technologies, they traditionally were mostly limited to advanced clinical laboratories. The pandemic drove investment in the advanced equipment required, leading to broader adoption and accessibility. Combined with technological advancements that made this equipment smaller, more affordable, and easier to use, reimbursement for molecular diagnostics became more widespread across many healthcare systems.
Innovation has also enhanced lateral-flow immunoassays and ELISA, with signal-amplification technologies improving sensitivity by up to 100-fold. Technological advancements like these, across both molecular and immunoassay-based diagnostics, are expected to drive convergence on speed, sensitivity, and cost (see Figure 2), though platform choice will still depend on analyte, setting, and diagnostic needs.
AI and machine learning (ML) further improve diagnostic capabilities by integrating test data with large volumes of patient records, supporting clinical decision-making. They can also be used for clinical genomic analysis (e.g., based on next-generation sequencing data) to classify disease, thus enabling more specific and accurate diagnoses. These technologies continue to evolve rapidly, unlocking new levels of diagnostic accuracy and identification of biomolecules. AI and ML can also help overcome challenges in detection and identification of signals of varying shape and quality. Accordingly, the use of these technologies in diagnostics has increased dramatically in recent years and is expected to continue even further in the coming years.
A major goal of precision health is selecting treatments that work for a particular patient from the outset, based on individual characteristics and detailed knowledge of treatment options. The likelihood of success for any particular treatment depends in part on effective patient stratification: grouping patients into cohorts based on factors like age, sex, ethnicity, disease, disease type/state, or presence of certain genes.
Supporting diagnostics play a critical role in this process by predicting how a patient will respond to a particular treatment. This is achieved by identifying biomarkers that indicate a specific tumor, trait, or disease type targeted by a given treatment. If the drug’s effectiveness relies on the presence of a particular biomarker, a diagnostic test that confirms its presence or absence enables clinicians to make highly informed treatment decisions.
Multimodal stratification and diagnosis is a growing trend, driven by an increasing understanding of the underlying causes of disease and the expanding availability of diagnostic tools. It involves using a combination of diagnostic tests to achieve highly precise diagnoses. In oncology, for example, multidisciplinary conferences or tumor boards then review the data to recommend tailored appropriate treatment plans.
To enable increasingly precise treatments, the interdependency between patient stratification and supporting diagnostics will drive closer collaboration and convergence among medtech, diagnostics, and pharmaceutical companies. All parties involved must consider new business models in areas such as intellectual property rights, revenue distribution, and risk-sharing.
One of the most important strategic decisions is whether to follow a companion or complementary diagnostic approach:
There is no one right approach. The decision depends on several factors, including therapeutic area, clinical indication, and the competitive situation. However, the choice has important implications for patient access as well as commercial strategy for both pharmaceutical and diagnostic companies.
Case study: History of companion & complementary diagnostics
The first US Food and Drug Administration (FDA)-approved companion diagnostic was the HercepTest immunohistochemistry (IHC) assay, approved in 1998 alongside the breast cancer drug Herceptin. Since then, the FDA has approved approximately 65 companion diagnostics, covering around 85 different drugs — most of them in oncology. Although the term “complementary diagnostic” had been used informally before, the first FDA-approved complementary diagnostic was the PD-L1 IHC 28-8 pharmDx assay, approved in 2015 to inform the use of Opdivo for patients with non-small cell lung cancer.
The original FDA label noted that PD-L1 expression detected by the assay “may be associated with enhanced survival from Opdivo,” setting a regulatory precedent for diagnostics that provide clinically meaningful information without being required for its safe and effective use. Since then, the FDA has approved several other complementary diagnostics. As both a driver and consequence of precision medicine, the continued expansion of companion and complementary diagnostics — including in therapeutic areas beyond oncology — is expected to play an increasingly vital role.
Once clinicians select a treatment for a patient, the role of diagnostics moves to monitoring how well the individual responds. If the chosen therapy fails to produce the desired effect, the best course of action is typically to adjust the treatment as soon as possible to improve outcomes and optimize the use of healthcare resources.
As our understanding of disease-related biomarkers grows, and high-precision diagnostic tools become more widely available, treatment monitoring is becoming increasingly effective. For example, instead of relying solely on tumor imaging or radiology scans, which may not immediately indicate whether a treatment is working, clinicians can monitor specific blood-based biomarkers that provide earlier indications of treatment effectiveness. Furthermore, blood tests can often be conducted in community settings, making monitoring more accessible and convenient for patients compared to hospital-based imaging.
Beyond tracking therapeutic response, insights from diagnostic monitoring can help clinicians understand why a therapy does or does not work. This enables more informed decisions about alternative therapies, further enhancing personalized treatment strategies.
While the successful conclusion of a patient’s treatment regimen is a key milestone, it often does not mark the end of a patient’s healthcare journey. In many cases, as with many cancers, there is a risk of relapse, making long-term monitoring essential. Similarly, for patients living with chronic diseases, ongoing monitoring of markers is critical to detect potential relapses early or to inform adjustment of treatment.
Early detection of relapse significantly improves the likelihood of favorable long-term outcomes. The discovery of new biomarkers has revolutionized early detection of relapse, particularly in oncology. Clinicians can now identify the presence or absence of certain biomarkers or genetic markers, providing valuable insights into disease progression and allowing for the early identification of cancer recurrence.
One example is prostate-specific antigen (PSA) testing for patients treated for prostate cancer. Although there has been controversy around testing PSA levels for screening purposes, it remains an effective tool for detecting recurrence and has been widely adopted in Europe, contributing to improved patient outcomes and quality of life.
However, as diagnostic technology advances, making it possible to detect new and lower levels of biomarkers, clinicians need guidelines on what to do when they are detected. For example, modern diagnostic tests can now detect much lower PSA levels, but it remains unclear how physicians should interpret those ultra-low levels. Does an exceptionally low level of PSA indicate a recurrence, or is there a specific threshold for this to be the case? Only once sufficient clinical evidence supports such low levels can clinical guidelines be updated, paving the way for broader adoption and market integration of these more sensitive tests.
Clinical diagnostics also play a significant role in monitoring chronic diseases such as autoimmune disorders, in which relapses can be sudden and unpredictable. Regular doctors’ visits often include diagnostic testing to assess disease activity and potential treatment changes.
Case study: Ultra-sensitive ctDNA testing for cancer monitoring
Emerging research has revealed a strong correlation between circulating tumor DNA (ctDNA) levels and cancer recurrence across multiple cancer types. To advance real-time treatment monitoring, companies such as Personalis and Natera have developed ultra-sensitive, ctDNA assays:
As ctDNA-based diagnostics continue to evolve, they are becoming indispensable for assessing treatment response, guiding therapy adjustments, and enabling truly dynamic, personalized care.
The rise of at-home and PoC diagnostic tools has transformed disease monitoring by enabling continuous self-monitoring. Mobile apps and wireless connectivity further facilitate real-time data sharing between patients and healthcare providers. These user-friendly at-home diagnostics devices empower patients to take a more active role in their care, reducing hospital visits and lessening the burden on healthcare systems as well as resulting in improved quality of life for patients.
As at-home diagnostics become increasingly popular, players in the space must differentiate themselves through:
To succeed, diagnostics developers must work closely with care providers and healthcare professionals to ensure tools integrate smoothly into care delivery and decision-making.
Diagnostics is a key driver of precision health. To lead in this space, companies must ensure that their solutions support clinical decision-making and address unmet needs throughout the patient journey, ultimately contributing to better long-term health outcomes. Success in precision diagnostics depends on several key factors:
By focusing on these key imperatives, diagnostics companies can not only differentiate themselves in a rapidly evolving market but also play a pivotal role in advancing precision medicine across the healthcare ecosystem.
By Dr. Ulrica Sehlstedt, Matilda Berg, Tyra Küller
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