Transcutaneous carbon dioxide (TcPCO2) monitoring provides a non-invasive alternative to measuring arterial carbon dioxide (PaCO2), making it valuable for various applications, such as sleep diagnostics and neonatal care. However, traditional transcutaneous monitors are bulky, expensive, and pose risks such as skin burns. To address these limitations, we have introduced a compact, cost-effective CMOS imager-based sensor for TcPCO2 detection by utilizing colorimetric reactions with metal–organic framework (MOF)-based nano-hybrid materials. The sensor, with a colorimetric sensing array fabricated on an ultrathin PDMS membrane and then adhered to the CMOS imager surface, can record real-time sensing data through image processing without the need for additional optical components, which significantly reduces the sensor’s size. Our system shows impressive sensitivity and selectivity, with a low detection limit of 26 ppm, a broad detection range of 0–2% CO₂, and strong resistance to interference from common skin gases. Feasibility tests on human subjects demonstrate the potential of this MOF-CMOS imager-based colorimetric sensor for clinical applications. Additionally, its compact design and responsiveness make it suitable for sports and exercise settings, offering valuable insights into respiratory function and performance. The sensing system’s compact size, low cost, and reversible and highly sensitive TcPCO2 monitoring capability make it ideal for integration into wearable devices for remote health tracking.

Wearable healthcare technologies are transforming the healthcare landscape by enabling remote, real-time health data collection, supporting early diagnosis, personalizing treatment plans, and reducing healthcare costs and medical burdens. Central to these advancements are wearable sensors, which continuously capture physiological data such as heart rate, temperature, activity levels, and biomarker concentrations. However, the large volume and complexity of this data demand effective processing to extract meaningful medical insights. Artificial intelligence (AI) and machine learning (ML) have significantly enhanced the capabilities of wearable sensors by enabling advanced data analysis, pattern recognition, and predictive modeling. AI-enhanced wearable sensors can detect early signs of health issues, such as heart attacks, chronic diseases, and mental health conditions like stress, often before clinical symptoms become apparent. This review examines the integration of AI/ML models with wearable sensors across physical activity recognition, stress assessment, cardiovascular monitoring, personal exposure monitoring, and sweat biomarker detection. Unlike prior application-centered reviews, we emphasize methodological and translational evaluation by comparing task formulations, sensing modalities, dataset scale, validation protocols, performance metrics, and deployment constraints across domains. We further discuss advanced architectures, multimodal fusion, explainable AI, edge deployment, privacy and regulatory considerations, and the translational gap between research prototypes and clinically deployable wearable AI systems.