A systematic literature review on integrating AI-powered smart glasses into digital health management for proactive healthcare solutions.

Journal: NPJ digital medicine
Published Date: Jul 5, 2025

Abstract

AI-powered smart glasses are emerging as a highly promising advancement in the field of digital health management, owing to their capabilities in real-time monitoring, chronic disease management, and personalized treatment planning. To comprehensively understand the current state of development, we systematically searched multiple databases, including Web of Science, PubMed, and IEEE Xplore, to collect relevant literature. This paper provides a systematic analysis of the current applications of smart glasses in healthcare, focusing on their potential benefits and limitations. Key issues discussed include user engagement, treatment adherence, data privacy, standardization, battery efficiency, clinical validation, and medical ethics. Our findings suggest that, supported by emerging clinical evidence, smart glasses have demonstrated significant improvements in areas such as assisted medical services, health management, anxiety alleviation in children, and telemedicine. By integrating multi-modal sensors, these devices are capable of accurately tracking certain physiological indicators and synchronizing real-time visual input, thereby enhancing the accuracy and timeliness of health interventions and medical services. Notably, some cutting-edge smart glasses have adopted advanced artificial intelligence algorithms, particularly large language models (LLMs) with context awareness and human-like interaction capabilities. These AI-powered glasses can offer real-time, personalized dietary and health management recommendations tailored to users' daily life scenarios. Building on these findings, this study further proposes a conceptual framework for proactive health management using smart glasses and explores future directions in technological development and practical applications. Overall, AI-enhanced smart glasses show great potential as a critical interface between healthcare providers and patients, poised to play a vital role in the future of personalized medicine and continuous health management.

Authors

  • Boyuan Wang
    School of Computer Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology, Macao, Macao SAR, China.
  • Ying Zheng
    Department of Ultrasound, Beijing Youan Hospital, Capital Medical University, Beijing 100000, China. Electronic address: xl2264@126.com.
  • Xihao Han
    Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, China.
  • Liang Kong
    School of Mathematics and Information Science & Technology, Hebei Normal University of Science & Technology, Qinhuangdao 066004, PR China. Electronic address: kongliangouc@hevttc.edu.cn.
  • Gexin Xiao
    National Institute of Hospital Administration (NIHA), Beijing, China.
  • Zunxiong Xiao
    The First Affiliated Hospital of Hunan University of Medicine, Huaihua, China. hyfyxzx@163.com.
  • Shanji Chen
    The First Affiliated Hospital of Hunan University of Medicine, Huaihua, China.

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