Machine Learning and Wearable Devices, Benefits and Challenges
Code: G-1847
Authors: Reyhaneh Norouzi Aval * ℗, Khalil Kimiafar, Seyyedeh Fatemeh Mousavi Baigi
Schedule: Not Scheduled!
Tag: Robotics in Surgery and Care
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Abstract:
Abstract
Background and aims: Machine learning (ML) and wearable devices have become critical components of modern healthcare, offering innovative solutions for monitoring, diagnosing, and treating various health conditions. This review explores the integration of ML with wearable devices, focusing on the benefits they bring to healthcare, alongside the challenges faced in their implementation and use. Method: A review was conducted by searching the keywords (Machine learning, wearable devices) in the title abstract in Web of Science, Scopus, and PubMed scientific databases on April 1, 2025. In this study, the Guidelines and Preferred Items of Systematic Review Studies (PRISMA) were followed. The quality of the studies was assessed using the JBI checklist. Studies with a score higher than 7 were analyzed. addressed the application of wearable technologies in health monitoring, predictive analytics using ML, and the benefits and challenges of such integrations. Both primary research articles and meta-analyses were included to ensure a broad perspective. Results: A total of 986 records were identified from databases. Among them, 42 records underwent eligibility screening. After applying inclusion and exclusion criteria, 13 studies were deemed eligible and included in this review. Results shown that The Wearable devices, in conjunction with machine learning algorithms, provide continuous monitoring of vital health data, allowing for 24/7 health tracking and early detection of potential health issues. The combination of wearable devices and machine learning has been particularly beneficial in managing chronic diseases such as diabetes, hypertension, and heart disease. However, there are challenges to overcome. Privacy and security concerns remain significant, as the sensitive health data collected by wearable devices must be protected. Additionally, the accuracy of the data can be affected by various factors such as the user’s physical state and environmental conditions, which can lead to issues with data interpretation. Finally, system integration and the need for global standards to ensure compatibility with existing healthcare systems are key challenges for optimizing the use of wearable technologies and machine learning in healthcare. Conclusion: Machine learning, combined with wearable devices, offers significant potential for transforming healthcare through continuous monitoring and personalized treatment.
Keywords
Machine Learning, Wearable Devices, Healthcare Technology
