Securing AI-Enabled IoT Healthcare Devices: Practical Solutions for Protecting Patient Data

Abstract

AI provides a biometrics authentication system to enhance a patient’s security, which requires unique physiological and behavioral characteristics to access it easily, and AI facilitates patient data through homomorphic encryption, differential privacy, and federation learning, allowing data to be analyzed and shared without exposing sensitive information. AI analyses user behavior patterns to detect potential insider threats or unauthorized access to patient data. The study highlight the centers on the security issues in IoT-based healthcare systems and presents a comprehensive framework designed to safeguard patient data. The study depicts the use of a method of a systematic literature review (SLR) to extract results and analyze unique security risks and their association with AI that enables IoT devices in healthcare. Furthermore, the results showed that implementation of continuous monitoring and audit mechanism is to respond and detected to its security incident implementation of continuous monitoring and audit mechanism is to respond and detected to security incident implementation of continuous monitoring and audit mechanisms is to respond and detect security incidents promptly. In conclusion, the given article addressed IoT solutions in healthcare, such as interoperability challenges and resource constraints. In an intrusion detection system, log monitoring irregularity detection is helpful for identification of unauthorised identification of unauthorized access for suspicious activities. Overall, the adoption of AI enables healthcare to rely on collecting and storing large patient data volumes. As a result, the data can be vulnerable to breaches, unauthorized access, and misuse.

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