Building Trustworthy AI systems for secure digital health services

Abstract

Nowadays, artificial intelligence (AI) is rapidly utilized in digital health services, which secure systems to safeguard patient data and ensure reliable service delivery. Our methodology incorporates authoritative artificial intelligence (AI) algorithms, strong encryption leads to an opportunity for the development of trustworthy systems, and adherence to healthcare standards like the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA).Particularly, we utilized convolutional neural networks (CNNs) for image analysis and natural language processing (NLP) to understand patient data, to determine the requirement for security, the nature of the data processed by NLP, computer vision, and acoustic AI, the underlying deep neural network topologies, the difficulty of assaults, and the perceptibility of attacks by humans are all examined. We deployed recent encryption standards to protect patient data transport and storage. Various studies and use cases reveal gains in data security, system reliability, and user trust, with findings indicating increased operational efficiency, decreased errors, and high compliance with regulatory regulations. These results show that our systems can considerably improve the security and efficiency of AI-powered digital health services.

Ähnliche Arbeiten