ENSURING DATA INTEGRITY IN AI-DRIVEN HEALTHCARE APPLICATIONS

Abstract

The adoption of Artificial Intelligence (AI) in healthcare has revolutionized patient care, diagnosis, and treatment planning. However, ensuring data integrity remains a significant challenge in AI-driven healthcare applications. Data integrity is essential for maintaining accurate, reliable, and secure healthcare information, which forms the foundation of AI systems. This study investigates the critical challenges and solutions for preserving data integrity in AI-driven healthcare applications. By reviewing existing literature and employing a qualitative methodology, the research identifies key threats to data integrity and evaluates best practices for mitigating these risks. The results highlight the importance of robust data management, encryption, and ethical AI design in safeguarding healthcare data. This paper underscores the need for comprehensive strategies to ensure the reliability and trustworthiness of AI systems in healthcare.