Going Digital: Emerging potential of Digital Biomarkers and AI/ML in Healthcare

Digital biomarkers and AI/ML in healthcare are redefining how biomarker discovery, drug development, and personalized medicine converge in modern medical research. If you are trying to explore the nexus between biomarker discovery, AI/ML advancements, and digital transformation in medicine, then you are in the right place.

Welcome to the forefront of revolutionizing innovation in drug discovery, development, and healthcare!

Over the years we have seen how digitization in healthcare and personalized treatments both have led to groundbreaking inventions. One such advancement is the discovery of digital biomarkers powered by the capabilities of Artificial Intelligence (AI) and Machine Learning (ML). In this blog we talk about how the convergence of AI and ML technologies with the quest for digital biomarkers has sparked a paradigm shift in drug discovery and digital medicine.

Biomarkers: The foundation of medical insights

Traditionally, biomarkers are crucial for medical diagnosis and prognosis. However, validating new biomarkers poses challenges in drug discovery. Traditional methods like immunohistochemistry (IHC) need careful optimization for visualizing cancer-related protein markers.

While conventional approaches have advanced our understanding of disease biology, modern methodologies, utilizing digital technologies and computational tools, are poised to revolutionize precision medicine (see Figure 1).

Figure 1: Traditional Vs. Recent Approaches in biomarker discovery

Traditional biomarkers, such as blood pressure and tumor markers like prostate-specific antigen (PSA), provide valuable diagnostic information but have limitations in temporal resolution and require periodic sampling. Digital biomarkers, derived from everyday digital devices like wearables and mobile apps (Figure 2), offer non-invasive, real-time insights into heart rate, sleep patterns, and activity levels.

These indicators leverage individuals’ digital footprints, encompassing physiological parameters, behavioral patterns, and environmental effects. Utilizing wearable devices in clinical trials reduces inconvenience for participants while providing a continuous view of physiological data essential for assessing a drug’s impact.

Figure 2: Digital Biomarkers (Source: SmokeBeat Somatix, Smart-watch by Apple, Face2Gene FDNA, Owlet Smart Sock)

Continuous glucose monitoring (CGM) systems utilize wearable sensors to track blood glucose levels in diabetic patients, enabling personalized diabetes management. SmokeBeat, a mobile application by SomatixTM, employs smartphone sensors to analyze sound and hand-to-mouth movements, identifying smoking events.

Key players like ActiGraph, Fitbit Health Solutions, Biogen, F. Hoffmann-La Roche Ltd., and Huma drive innovation in the digital biomarkers market. Increasing adoption highlights the growing relevance of digital biomarkers and AI/ML in healthcare.

Software as a Medical Device (SaMD) empowers clinicians with tools for diagnosis, treatment planning, and patient monitoring. SaMD plays a pivotal role in analyzing complex datasets from wearables, mobile health apps, and electronic health records (EHRs).

Integrating digital biomarkers into healthcare systems holds promise for enhancing patient care. Leveraging digital technologies, researchers can accelerate drug discovery, development, and repurposing efforts through data-driven drug discovery.

AI/ML roadmap to biomarker discovery landscape

AI/ML empowers biomarker discovery by unraveling complexities in multi-omics datasets such as genomics, proteomics, and metabolomics (Figure 3). Over the last two decades, significant growth in AI-driven biomarker research reflects the impact of digital biomarkers and AI/ML in healthcare.

In genomics, AI algorithms identify genetic variations with precision, supporting disease diagnosis. In proteomics, AI enhances protein structure prediction and mass spectrometry analysis, advancing drug discovery.

In metabolomics and epigenetics, AI techniques detect disease-associated signatures, while multi-omics integration enables holistic disease understanding through predictive modeling.

Figure 3: AI/ML based workflow for biomarker discovery

In drug discovery, AI bridges biomarker identification to drug design, optimizing drug combinations, repurposing molecules, and enabling personalized medicine strategies through bioinformatics solutions.

AI with digital biomarkers

AI revolutionizes digital biomarkers by enabling precise insights and personalized interventions. Platforms leveraging AI support real-time patient monitoring, early detection of abnormalities, and efficient clinical trial execution.

In Drug Discovery and Development (DDD), AI-driven digital biomarkers improve clinical trial efficiency, reduce sample sizes, and support real-world evidence generation. Continuous monitoring outside clinical environments provides insights into long-term drug effects and adverse events.

As AI technologies progress, AI-driven omics analysis and digital biomarkers will play pivotal roles in precision medicine and digital healthcare.

Excelra’s edge on AI/ML-induced biomarker identification

Excelra integrates evolving technologies for biomarker discovery, offering robust support for AI/ML-enabled approaches. In oncology, in-house ML models leverage expression data and advanced feature selection methods to predict drug response biomarkers with high accuracy.

Figure 4: Excelra’s Workflow for predictive biomarker identification

Excelra further utilizes AI/ML algorithms to predict drug combinations and disease indications, demonstrating strong performance across large datasets. This expertise aligns with Excelra’s broader capabilities in scientific informatics and scientific data management.

What does the future hold?

The future of healthcare is being reshaped by digital biomarkers and AI/ML advancements. Integrating multi-omics data with ML enables early disease detection and personalized interventions, despite challenges such as data quality and model interpretability.

Explainable AI (XAI) is expected to improve trust and transparency in AI-driven biomarker discovery, enhancing adoption in clinical practice and research.

Conclusion

The dynamic interplay of biomarker discovery, with a focus on digital biomarkers and AI/ML in healthcare, opens unprecedented avenues for transformative drug discovery, development, and patient care. The shift toward digital biomarker-driven insights empowers clinicians to deliver precision and personalized medicine, turning innovation into reality.