Cybersecurity for Analyzing Artificial Intelligence (AI)-Based Assistive Technology and Systems in Digital Health

Abstract

Assistive technology (AT) is increasingly utilized across various sectors, including digital healthcare and sports education. E-learning plays a vital role in enabling students with special needs, particularly those in remote areas, to access education. However, as the adoption of AI-based AT systems expands, the associated cybersecurity challenges also grow. This study aims to examine the impact of AI-driven assistive technologies on cybersecurity in digital healthcare applications, with a focus on the potential vulnerabilities these technologies present. Methods: The proposed model focuses on enhancing AI-based AT through the implementation of emerging technologies used for security, risk management strategies, and a robust assessment framework. With these improvements, the AI-based Internet of Things (IoT) plays major roles within the AT. This model addresses the identification and mitigation of cybersecurity risks in AI-based systems, specifically in the context of digital healthcare applications. Results: The findings indicate that the application of the AI-based risk and resilience assessment framework significantly improves the security of AT systems, specifically those supporting e-learning for blind users. The model demonstrated measurable improvements in the robustness of cybersecurity in digital health, particularly in reducing cyber risks for AT users involved in e-learning environments. Conclusions: The proposed model provides a comprehensive approach to securing AI-based AT in digital healthcare applications. By improving the resilience of assistive systems, it minimizes cybersecurity risks for users, specifically blind individuals, and enhances the effectiveness of e-learning in sports education.

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