**Background:** Traditional healthcare systems face barriers in chronic disease monitoring, personalized medicine, and early intervention, especially in resource-limited areas. Smart wearable sensors—electronic devices worn on the body that collect, process, and transmit physiological data—offer a solution by enabling continuous, real-time monitoring. This review summarizes the clinical applications of wearable sensors in various medical scenarios, including skin diseases, cardiovascular diseases (CVDs), abnormal human motion, endocrine and metabolic disorders, drug concentration monitoring, and heavy metal toxicity detection. The integration of IoT, 5G, cloud computing, and AI can transform raw sensor data into clinically interpretable information.
**Methods:** The authors conducted a narrative review of the literature, focusing on recent developments in wearable sensor technologies. They categorized applications by body system and disease type, describing specific sensor architectures, target analytes, and clinical validations. The review includes a summary table of 30 wearable systems, detailing their location, sensing type, target analytes, sensor architectures, features, and medical applications. The authors also discuss challenges and future perspectives.
**Key Results:**
- **Skin diseases:** Wearable sensors can objectively quantify pruritus (e.g., ADAM device), measure skin hydration in atopic dermatitis and psoriasis (SHS), detect tyrosinase for melanoma screening (bandage and microneedle sensors, bio-FETs), and monitor wound healing via temperature, thermal conductivity, pH, and AI-based staging (EES, smart dressing, FLEX-AI system).
- **Cardiovascular diseases:** Dry electrodes (e.g., self-adhesive stretchable electrodes) and electronic textiles (CNTT) enable ECG monitoring. Textile triboelectric sensors with machine learning estimate systolic and diastolic blood pressure (validated against commercial cuff). Flexible ultrasonic devices (240 μm thickness) monitor central blood pressure from carotid, brachial, radial, and dorsalis pedis arteries. Ultra-flexible reflective pulse oximeters (e.g., organic pulse oximeters) measure SpO2 with ultralow power consumption (tens of microwatts). Ambient light oximeters eliminate LEDs, reducing power use.
- **Abnormal human motion:** Neck motion detectors (triboelectric sensor group with deep learning) recognize 11 neck motion classes with >90% accuracy. Flexible resistive sensor networks track shoulder motion. Electronic skin with deep neural network decodes five finger movements from wrist. Upper limb rehabilitation garments (Zishi system) with inertial sensors improve training motivation. Lower-limb motion-capturing system (MC-EH-HL) detects gait features (reduced strength, highly abnormal, slightly abnormal) and Parkinsonian gait patterns (normal gait, loss of stride, freezing of gait).
- **Endocrine and metabolic abnormalities:** Sweat-based sensors detect glucose (tattoo-based, strong correlation with commercial meters), ammonium (tattoo-based potentiometric), tyrosine and uric acid (laser-engraved graphene sensor with DPV), sodium and potassium (textile-based multi-ion sensor), calcium and pH (wearable electrochemical device), and cortisol (MIP-based sensors, touch-based noninvasive sensor). Contact lens sensors detect glucose, pH, protein, and nitrites in tears.
- **Drugs, ethanol, and heavy metals:** Wearable platforms monitor caffeine (DPV sensing), levodopa (nanodendritic sweatband), and ethanol (iontophoretic-biosensing tattoo). Heavy metal sensors detect zinc (bismuth/Nafion film), copper, lead, cadmium, mercury (SWASV microsensor array), and copper (microfluidic nanosensor).
**Clinical Implications:** Wearable sensors offer objective, continuous, noninvasive monitoring that can improve early diagnosis, treatment adjustment, and patient outcomes. They facilitate home-based care, reducing hospital burden and enabling personalized medicine. However, challenges include material comfort and safety (e.g., acrylate allergens), portability and battery life, accuracy and stability (e.g., biomarker contamination, motion artifacts), data identification and analysis (need for AI and clinical standards), and patient data security (encryption and authentication). Future advancements in materials, signal processing, AI, IoT, and 5G are expected to overcome these barriers, making remote healthcare and personalized medicine a reality.