Digital Biomarkers: The Key to Delivering on the Promise of Personalized Medicine

Digital Biomarkers: The Key to Delivering on the Promise of Personalized Medicine
Digital Biomarkers: The Key to Delivering on the Promise of Personalized Medicine


As the healthcare industry strives toward precision medicine, it will be essential for care providers to gain an intimate view of a patient’s response to treatment to determine its impact on their quality of life. This shift is already starting in the clinical research setting, where AI-powered digital biomarkers are delivering key insights into patients’ health to inform personalized patient support and objective conclusions about a drug’s impact. As visual and auditory digital biomarkers continue to evolve and become more commonplace in everyday patient care, clinicians can gain a more comprehensive understanding of a patient’s lived experience with a disease and ultimately convert that knowledge into predictive power to transform how care is delivered.

What are digital biomarkers and how are they used today?

While the words “digital biomarkers” often inspire thoughts of smartwatches or other wearables, one of the greatest opportunities to customize care is through audio and visual digital biomarkers that can be captured through the front-facing camera on a patient’s smartphone. By analyzing patients’ behavior through technology that most people use every day, we can capture digital biomarkers such as facial characteristics, vocal patterns, and movement, and identify trends over time. This enables clinicians to remotely identify slight shifts in a patient’s health and response to therapy that may otherwise be too subtle for care providers to detect in an in-person examination or missed between visits.

Many diseases manifest visual and auditory elements in their symptoms, making them great use cases to explore the sensitive and objective data capture these AI-powered digital biomarkers can provide. For example, patients with depression can demonstrate less facial expressivity, a slow, quiet voice, and reduced overall movement. Having patients respond to basic prompts or complete simple tasks each day via a smartphone app can help care providers tap into health indicators like whether patients are smiling or their vocal inflections to get a better sense of their overall mood and well-being. These insights may ultimately guide more personalized treatment decisions or alert clinicians to which patients may need more immediate hands-on support with managing their condition.

Matching the right patients with the right treatment

By capturing these subtle patient behaviors over time, visual and auditory digital biomarkers have the potential to help determine if a drug is the right fit for a patient and vice versa. The depth of patient-level data AI-powered digital biomarkers provide allows clinicians to cut through the diversity of symptom expressions, inform how multiple comorbidities may be interacting, and determine whether a drug is working as it should for a patient – all to inform timely, personalized adjustments to a patient’s care plan.

Patients with immunological disorders provide a great case study to showcase digital biomarkers’ precise, sensitive data capture. Symptoms such as fatigue, pain, or depression that accompany immunologic disease may substantially negatively impact quality of life, and be highly variable over the course of illness and treatment.  By gaining a frequent, personalized window into a patient’s day-to-day experience through digital biomarkers, combined with additional patient data such as medication adherence and other data, we may be able to identify responders vs. non-responders sooner for clinicians to understand what changes to their care plan may be needed.

Stroke can also be a complex condition for patients to deal with and for clinical researchers to develop effective therapies for, as it is challenging to accurately keep track of symptom changes between doctor visits to understand if the drug is successfully managing their condition. Using AI-powered digital biomarkers, physicians could analyze a stroke patient’s symptoms over time and build a trajectory of their health, enabling them to determine whether a patient is effectively responding to their medication much earlier on. The more we can understand the nuances of a patient’s condition and response to treatment, the more we can help match the right patient with the right drug at the right point in their disease.

The future of digital biomarkers: Predicting risk and quality of life improvements

As digital biomarkers and AI advance and are fed more high-quality, patient-level data, they have great potential to drastically change the standard of patient care. Digital biomarkers today serve as a valuable monitoring tool, providing details about a patient’s general health and response to treatment. But, as technology continues to develop, they could fuel predictive insights about a patient’s future health outcomes and disease trajectory, as well as demonstrate measurable quality of life improvements.

AI-powered digital biomarkers could pave the way for risk assessments to help predict future health events and prompt interventions before they occur. For example, in diseases with a high risk of flare-ups like asthma, the combination of environmental data, such as weather and pollen count, and indications of patient health captured through digital biomarkers, may enable tools to visualize the trajectory of a patient’s symptoms to predict breakthrough events and proactively mitigate them before they take place.

Paving the way for precision medicine to become standard-of-care

While we are just scratching the surface of personalized care now, digital biomarkers could be a key to unlocking the full potential of precision medicine. Starting in the clinical research setting, audio and video digital biomarkers have the potential to help define the specific characteristics of the patient population and highlight the potential benefit of investigational therapies.  This data could then be used to inform decision-making around the clinical pathway for that medication’s development, as well as start to lay the groundwork for understanding the medication’s use post-launch in real-world patients going forward.

As these new avenues for personalized care continue to be discussed and developed, it is essential to ensure that algorithms are built using diverse data to ensure accurate results for everyone who uses it. With unbiased algorithms developed from diverse data sets, AI-powered digital biomarkers are well-positioned to enable personalized treatments and interventions for all patients, paving the way for a future of precision medicine as standard-of-care.

Photo: Anastasia Usenko, Getty Images



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