AI wearable biosensors transform early detection and management of major diseases

The authors argue that, collectively, AI-enabled wearables represent the next critical layer of global health infrastructure. Their ability to deliver continuous physiological and biochemical data, combined with AI’s capacity to interpret complex signals, makes them a powerful alternative to traditional diagnostics that rely on invasive sampling, expensive equipment, and periodic clinic visits. As chronic disease burdens rise, remote monitoring becomes essential, and AI-driven wearable biosensors are positioned to fill this gap.

Artificial intelligence–powered wearable biosensors are rapidly becoming a central force in healthcare, redefining how major diseases are detected, monitored, and managed. A new review highlights an accelerating shift toward remote, real-time health intelligence as global demand grows for noninvasive and continuous diagnostics.

The study, “Artificial Intelligence-Based Wearable Sensing Technologies for the Management of Cancer, Diabetes, and COVID-19,” published in Biosensors, evaluates five major sensing categories, electrochemical, colorimetric, chemical, optical, and pressure-strain sensors, and examines their clinical potential, technological barriers, and real-world value across three of the world’s highest-burden diseases.

The authors argue that, collectively, AI-enabled wearables represent the next critical layer of global health infrastructure. Their ability to deliver continuous physiological and biochemical data, combined with AI’s capacity to interpret complex signals, makes them a powerful alternative to traditional diagnostics that rely on invasive sampling, expensive equipment, and periodic clinic visits. As chronic disease burdens rise, remote monitoring becomes essential, and AI-driven wearable biosensors are positioned to fill this gap.

How AI turns wearables into continuous diagnostic platforms

The first major question the study addresses is how wearable biosensors evolve from simple data collectors into intelligent diagnostic systems when integrated with AI. The authors classify wearable sensing technologies into five main categories, each contributing distinct clinical capabilities.

Electrochemical biosensors have become foundational across wearable platforms. These sensors measure metabolites and biomarkers, such as glucose, lactate, electrolytes, and nutrients, from easily accessible biofluids like sweat, saliva, tears, urine, or interstitial fluid. Recent improvements in nanomaterials, including metal–organic frameworks, metal dichalcogenides, carbon nanostructures, and flexible conductive polymers, have enhanced sensitivity while allowing sensors to bend, stretch, and conform to the skin. Hydrogels and soft substrates support long-term adhesion, improving comfort and stability during daily activity.

Colorimetric sensors form another key pillar in wearable diagnostics. These platforms use visible color changes to signal biochemical fluctuations. Unlike older color-change strips, modern systems integrate microfluidics, textiles, or contact lenses capable of transporting or concentrating analytes. AI models are used to correct lighting variations, adjust for environmental conditions, and interpret subtle color shifts, converting them into quantitative measurements. This capability reduces user subjectivity and elevates colorimetric sensing to clinical-grade reliability.

Chemical wearables broaden the diagnostic spectrum by detecting gases, electrolytes, and volatile compounds that can indicate respiratory, renal, hepatic, or metabolic dysfunction. Nanomaterial-based chemiresistors amplify tiny chemical interactions, but these signals are often noisy or ambiguous. AI models help distinguish true biochemical changes from environmental interference, making these platforms more robust.

Optical wearables, often found in smartwatches or patch systems, measure parameters such as heart rate, blood oxygen saturation, glucose, blood pressure, and perfusion through light–tissue interactions. The study highlights rapid advances in optoelectronics that expand the types of biomarkers detectable via optical pathways. AI algorithms play a critical role by filtering noise, managing motion artifacts, and linking optical signatures with health events.

The fifth category, pressure and strain sensors, contributes mechanical insights from the human body. These wearables monitor subtle changes in arterial pulse waves, joint movement, respiration, and muscle contraction. Many rely on organic nanomaterials that respond to stretching, bending, or compression. AI models classify the patterns these sensors capture, enabling early identification of cardiac anomalies, respiratory irregularities, or mobility-related health issues.

Across all five categories, the authors emphasize that AI is not an optional add-on. The data produced by continuous sensing, high-frequency, multi-channel, and highly variable, cannot be managed manually or through simple algorithms. Machine learning and deep learning models allow wearables to transition from passive trackers to proactive diagnostic systems capable of identifying early deviations and forecasting health risks.

How AI wearables transform management of COVID-19, diabetes, and cancer

COVID-19, diabetes, and cancer represent three of the most pressing global health challenges, and wearable technologies have demonstrated significant gains in each.

In COVID-19, AI-enhanced wearables proved essential for monitoring physiological changes that occur before symptoms appear. Devices tracking heart rate and heart rate variability can identify early signs of physiological stress associated with infection. AI models analyze resting heart rate changes that may precede symptom onset, providing early warning signals useful for screening and community-level monitoring. Wearable oximeters deliver continuous oxygen-saturation data, enabling detection of silent hypoxia, a hallmark complication of COVID-19 where oxygen levels drop without noticeable symptoms. Temperature-monitoring wearables, including smart patches and textiles, support early fever detection and remote triage. Respiratory wearables and even non-contact sensing systems capture breathing patterns that AI can classify into early respiratory distress signatures. Multi-sensor devices that integrate heart rate, SpO₂, temperature, and respiration create composite models that help predict deterioration or monitor long COVID.

For diabetes, the study highlights the crucial shift from episodic glucose monitoring to continuous glucose monitoring (CGM). Wearable CGM systems, driven by electrochemical or optical technologies, track glucose trends in real time. AI models forecast glucose fluctuations minutes or hours ahead, offering predictive protection against dangerous highs and lows. These algorithms also support semi-automated insulin dosing and contribute to the development of closed-loop artificial pancreas systems. AI corrects for confounding factors such as temperature, skin conditions, sweat interference, or motion artifacts, increasing the reliability of CGM readings. Combined with activity and dietary data, machine learning models provide highly individualized metabolic insights that support long-term disease management.

In cancer, wearable biosensors are presented as emerging tools for noninvasive monitoring. While traditional cancer diagnostics rely on imaging, biopsies, and centralized laboratory testing, AI-supported biosensors allow continuous assessment of biomarkers tied to tumor metabolism, treatment response, or early relapse indicators. Some wearables track thermal or vascular changes associated with tumor growth. Others incorporate biochemical or optical sensors capable of identifying cancer-specific molecular signatures. These real-time data streams help detect subtle physiological changes that are often impossible to capture through periodic clinic visits. Wearables also reduce the burden of frequent hospital monitoring during chemotherapy or targeted therapies, supporting patient comfort and improving adherence to treatment protocols.

Across all three diseases, the authors highlight how AI transforms wearable data into clinical value. Deep learning models classify patterns that humans cannot detect, prediction algorithms warn of deterioration before symptoms appear, and automated analyses support decision-making that would otherwise require extensive clinical infrastructure.

What challenges threaten large-scale deployment of AI-based wearables

The authors identify several technological, clinical, and ethical challenges that the field must overcome.

Sensor accuracy and long-term stability remain major obstacles. Wearables operating on the skin face motion, sweating, temperature variation, mechanical stress, and environmental contamination. These introduce noise and drift that degrade signal quality. The authors stress that improved materials, stronger calibration algorithms, and better signal-processing pipelines are essential for clinical reliability.

Another challenge is data privacy and security. Wearable devices continuously transmit sensitive health information, often through cloud platforms. Without robust encryption, secure architectures, and regulatory oversight, these data streams become vulnerable to breaches. AI models, especially those using cloud-based training, compound the risk by involving third-party environments and external servers.

Standardization is another significant hurdle. Wearables differ widely in sensing mechanisms, communication protocols, calibration practices, and data formats. This fragmentation makes it difficult to integrate wearable outputs into clinical information systems or electronic health records. Without standardized validation frameworks, regulators cannot easily assess clinical-grade reliability.

Cost and accessibility also remain concerns. Advanced materials, microneedle arrays, nanostructured sensing films, and continuous optical systems can be expensive to develop and produce. Clinical validation requires large trials, regulatory clearance, and manufacturing scale—barriers that slow adoption, especially in low-resource regions.

AI adds its own layer of complexity. Models must be trained on diverse populations to avoid bias. They must be interpretable enough for clinicians to trust their recommendations. Furthermore, wearable-AI systems must operate efficiently on local hardware without requiring continuous cloud access, particularly for real-time decisions.

Despite these challenges, the authors conclude that AI-enhanced wearable biosensors are advancing quickly and are positioned to become a core component of decentralized healthcare. Their ability to integrate continuous sensing with predictive analytics makes them one of the most promising technologies for early diagnosis, personalized treatment, and large-scale public health monitoring.