AI Enabled Smart Textiles: A Bibliometric and Thematic Analysis

The integration of artificial intelligence (AI) into smart textiles has created new opportunities in wearable technology, enabling real-time monitoring, personalized feedback, and enhanced functionality across domains such as healthcare, sports, and environmental sensing. However, challenges related to power management, durability, and data privacy have kept most of these technologies at the prototype stage. This study conducts a comprehensive bibliometric and thematic analysis of the AI-enabled smart textile literature published between 2015 and 2025 in the Web of Science (WoS) database. A total of 970 publications were examined, with bibliometric mapping performed using VOSviewer 1.6.20 and qualitative content analysis applied to the 20 most-cited articles. The findings reveal a sharp increase in publications after 2018, with China (258 publications) and the United States (209 publications) leading global scientific output. Research trends predominantly focus on health monitoring, activity recognition, wearable sensor optimization, and deep learning-based anomaly detection. Thematic analysis identified eight primary research clusters, and the results highlight the transformative potential of AI-integrated smart textiles in sustainability, health monitoring, and personalized care, paving the way for the transition from laboratory prototypes to real-world applications.