Ai-Powered Health Care: Balancing Data Utility And Patient Privacy

Abstract

Artificial intelligence (AI) has changed the landscape of healthcare systems, including medical diagnostic, treatment planning, and patient monitoring. Nonetheless, such a fast development provokes serious questions about patient privacy, especially in the age of big data and electronic health records. In this paper, the authors explore the twofold problem of making the most AI models useful with the data and ensuring the privacy of patients. It summarizes recent advances in privacy-preserving methods including differential privacy, federated learning and homomorphic encryption. A comparative analysis and a prototype implementation performed in the course of the study show that privacy-enhancing technologies can reduce the risks but there exists a trade-off between the model accuracy and the complexity of the resulting system. The study provides a conclusion with the suggestion of a balanced framework, which maximizes the utility of the data and privacy guarantees of AI-driven healthcare applications.

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