AI-BASED SMART TEXTILE WEARABLES FOR REMOTE HEALTH SURVEILLANCE AND CRITICAL EMERGENCY ALERTS: A SYSTEMATIC LITERATURE REVIEW

Abstract

The integration of artificial intelligence (AI) in smart textile wearables has revolutionized healthcare by enabling real-time, non-invasive monitoring of physiological parameters, predictive analytics, and automated decision-making for early disease detection and intervention. This systematic review examines the advancements, challenges, and regulatory considerations surrounding AI-powered smart textile wearables by analyzing 244 peer-reviewed studies selected from an initial pool of 1,264 articles published before 2023. The study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, ensuring a structured and transparent review process. Findings indicate that AI-enhanced biosensors integrated into smart textiles have significantly improved the accuracy and efficiency of health monitoring systems, particularly in areas such as cardiovascular health, diabetes management, neurological disorder detection, respiratory health surveillance, maternal health monitoring, and occupational safety applications. The review highlights that machine learning (ML) and deep learning (DL) models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have increased biosignal classification accuracy by up to 18%, reducing false-positive rates and enhancing clinical decision support. Furthermore, federated learning techniques have addressed algorithmic bias issues, improving the generalizability of AI-driven health assessments while preserving patient data privacy. However, despite these advancements, 32% of the reviewed studies reported challenges related to motion artifacts, environmental variability, and sensor calibration issues, which continue to impact data reliability in wearable medical textiles. Regulatory compliance remains a significant barrier, with 64% of studies highlighting the complexity of obtaining FDA pre-market approval (PMA) for AI-integrated medical wearables due to the evolving nature of AI models. Cybersecurity concerns also persist, as 22 reviewed studies identified risks associated with biometric data transmission and unauthorized access, reinforcing the need for stronger encryption protocols and standardized privacy frameworks. Despite these challenges, AI-driven smart textiles have demonstrated their effectiveness in reducing hospital readmissions, improving patient adherence to long-term health monitoring, and lowering overall healthcare costs by 19% through early disease detection and proactive medical intervention. As AI-powered smart textiles continue to evolve, addressing challenges related to sensor accuracy, regulatory oversight, cybersecurity, and interoperability with existing healthcare systems will be crucial to unlocking their full potential. This review underscores the transformative role of AI-integrated smart textile wearables in shaping the future of digital healthcare, enabling innovative, personalized, and data-driven healthcare solutions that optimize clinical workflows, enhance patient outcomes, and drive forward the next generation of intelligent health monitoring technologies.