Integration of the Internet of Things (IoT) and Artificial Intelligence (AI) in Remote Monitoring of Occupational Skin Diseases: A Literature Review
Keywords:
Internet of Things, Artificial Intelligence, Occupational Skin Diseases, Remote Monitoring, Occupational Health, Occupational DermatologyAbstract
Occupational skin diseases are a significant occupational health issue, with a prevalence of up to 35% in the construction industry and 30% in healthcare. The annual economic impact of these diseases is estimated at $1-2 billion in the US. The integration of the Internet of Things (IoT) and Artificial Intelligence (AI) offers revolutionary potential for improving early detection and sustainable management of these conditions. Objective: This literature review aims to explore the state-of-the-art integration of IoT and AI in remote monitoring of occupational skin diseases, with a focus on technological innovations, practical implementation, and occupational health implications. Methods: A systematic search was conducted in the PubMed, IEEE Xplore, Scopus, and Web of Science databases for articles published between 2019-2024. Inclusion criteria include applications of IoT and AI in occupational dermatology. The selection process involved screening titles/abstracts and full-text reviews. Results: This review identified cutting-edge IoT technologies, including L'Oréal's skin sensor patches and DermaTherapy® smart textiles, as well as integrated IoT systems for monitoring work environments. AI algorithms, such as those from Google and IBM Watson, demonstrated up to 95% accuracy in detecting and diagnosing skin diseases. Case studies showed a 50% reduction in diagnosis time and a 40% decrease in dermatitis incidents in the chemical industry. The main challenges include data privacy and technology adoption, with blockchain identified as a potential solution for data security. Conclusion: The integration of IoT and AI offers a transformative approach to managing occupational skin diseases, with a potential Return on Investment (ROI) of 200-300% within 3 years. Initial implementations show promising results, but further research is needed to address technical and ethical challenges and to develop AI-based personalized prevention strategies.
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