From Reactive to Proactive Healthcare: Synergizing Wearable Biomarkers and Machine Learning in Digital Therapeutics

Wearable-Derived Digital Biomarkers in Preventive and Personalized Medicine: Promise, Evidence, and Barriers to Clinical Translation

Wearable technologies now permit near-continuous measurement of physiological and behavioral parameters under free-living conditions. Combined with advances in artificial intelligence (AI), these devices support the development of wearable-derived digital biomarkers that may shift healthcare from a reactive to a preventive and personalized model. This narrative review synthesizes current evidence on the technological foundations, AI-based signal processing, clinical applications, implementation challenges, and future directions of wearable-derived digital biomarkers. Evidence supports their use across cardiovascular disease, diabetes and obesity, neurological and mental health conditions, sleep and respiratory medicine, and remote patient monitoring, where continuous data streams can inform early detection, individualized risk prediction, treatment optimization, and clinical decision-making. We propose a conceptual framework describing how wearable sensing, continuous physiological data acquisition, and AI-based analytics translate into clinically actionable digital biomarkers. At the same time, we argue that enthusiasm has outpaced evidence: few candidate biomarkers have undergone prospective validation in diverse populations, analytical performance varies substantially across devices and skin tones, and demonstration of improved clinical outcomes remains rare. Data quality, validation, standardization, interoperability, algorithm transparency, privacy, cybersecurity, regulatory oversight, and equitable access all constrain clinical adoption. Emerging developments in explainable AI, multimodal data integration, digital twins, and predictive analytics may address some of these constraints. Wearable-derived digital biomarkers hold genuine potential for proactive, patient-centered, data-driven care, but realizing that potential will depend less on new sensors than on rigorous validation, standardization, and equitable implementation.

Digital Biomarkers and Wearable Bioelectronics Across Neurological, Cardiopulmonary, and Mental Health Conditions: Clinical Evidence and Barriers to Adoption

Chronic diseases now account for the majority of global morbidity, yet the clinical tools used to track them remain largely episodic, relying on brief in-clinic snapshots rather than a patient's lived, day-to-day physiology. Wearable bioelectronics and the digital biomarkers (dBMs) they generate have been proposed as a remedy, but the evidence supporting their clinical utility is scattered across disparate disease literatures. We conducted a narrative synthesis of clinical, materials-science, and computational evidence on wearable-derived digital biomarkers across neurological, cardiopulmonary, inflammatory, wound-care, and mental-health conditions, drawing on peer-reviewed studies identified through the source literature and organized according to a reproducible, PRISMA-informed search and appraisal framework. Passive sensing demonstrated strong ecological validity: smartwatch-based tremor and gait metrics tracked closely with clinician-rated Parkinson's disease scales, under-mattress bed sensors detected pre-clinical heart-failure decompensation, and closed-loop smart bandages achieved autonomous, infection-triggered antibiotic release. Deep-learning models incorporating autoencoder-derived embeddings predicted depressive symptom severity with a correlation of r = 0.73, outperforming handcrafted statistical features alone. Materials innovations, particularly zwitterionic anti-fouling coatings, reduced biosensor signal drift to under 2% per day. Digital biomarkers have moved credibly beyond proof-of-concept, but scaled clinical adoption remains constrained by biosensor durability, limited external validation, fragmented health-record interoperability, and inequitable access. A coordinated engineering, regulatory, and equity-oriented roadmap is required before these tools can be trusted as everyday clinical instruments.

Digital Pain Medicine in the Era of Precision Healthcare: Integrating Artificial Intelligence, Wearable Biosensors, and Predictive Analytics for Personalized Pain Management

Digital pain medicine is taking shape as a precision health care model to help in pain management. This comprehensive review examines the integration of artificial intelligence, wearable biosensors, digital phenotyping, predictive analytics, and personalized therapeutic strategies in modern pain management. Current evidence suggests that wearable technologies and remote monitoring systems can capture continuous physiological and behavioral data, offering a more objective view of pain beyond episodic clinical encounters and self-reported scales. Artificial intelligence and machine learning can transform multimodal data into clinically useful insights for pain detection, classification, risk stratification, treatment planning, and decision support. Predictive analytics, pharmacogenomics, biomarkers, and digital twin technologies further support individualized care pathways across chronic, neuropathic, cancer-related, postoperative, pediatric, and telehealth-enabled pain conditions. Yet, implementation remains hampered by challenges related to data quality, interoperability, privacy, governance, algorithmic transparency, clinical validation, regulatory oversight, and equitable access. Future progress will require rigors validated, explainable, secure, clinician-guided digital systems that augment, rather than replace, clinical judgement. Digital pain medicine offers a path to more proactive, objective and patient-centric pain care, but its success will depend on responsible translation into real-world clinical practice.  

AI-Powered Wearable Devices for Real-Time Health Monitoring: Technologies, Challenges, and Emerging Trends

This work connects biomedical signal capture, on-device AI, clinical translation, and digital ethics to delineate a roadmap for frontier AI wearables, and proposed edge-aware transformer-based models, privacy-preserving analytics, and global multimodal sensing advance intelligent, scalable, and fair AI-driven health monitoring systems.

Smart Healthcare Systems Using Predictive Machine Learning

Smart healthcare systems can be viewed as a paradigm shift in the contemporary medical practice, since modern placement of sensing technologies, electronic health records (EHRs), and predictive machine learning (ML) technologies have become a common practice that facilitates proactive, personal, and efficient healthcare delivery. Such trends as the growing rate of chronic illnesses, the aging trend, and escalating healthcare costs have led to a desperate demand of smart systems able to diagnose and predict risks early, as well as make the optimal clinical decision. Predictive machine learning predictive models are models based on historical and real-time healthcare data that reveal the latent patterns, predict the disease progress, and assist clinicians to make evidence-based decisions. In this paper, predictive machine-learning has been adopted to analyze in detail the concept of smart healthcare systems, which involve system architecture, data acquisition, feature engineering, model development, and performance evaluation. The suggested framework combines the wearable sensor data, clinical records, and demographic information into a single analytics pipeline. Some of the machine learning algorithms under analysis and supervised or unsupervised, like logistic regression, support vector machines, random forests, gradient boosting, and deep neural networks, are discussed in the context of healthcare prediction tasks, such as disease risk prediction and hospital readmission prediction and patient outcome prediction. Moreover, the paper also touches on the issues associated with data quality, privacy, security, model interpretability and ethical issues. Recent experimental findings indicate that predictive ml-based healthcare systems are much better than conventional rule-based healthcare systems in terms of prediction accuracy and decision support. The results also show the promise of predictive machine learning to turn healthcare into either a reactive treatment or a preventive, personalized healthcare, as part of the vision of smart and sustainable healthcare ecosystems.

[From Sensing to Decision: Leveraging Technology to Enhance Nursing Clinical Judgment].