Paper Title
"Preserving Data Integrity and Privacy in AI-Driven Healthcare Fraud Detection Systems - Challenges and Best Practices"

Abstract
Public health services are rapidly becoming digital, necessitating stringent data security protocols. Electronic health records and telemedicine platforms are just two examples of the many digital tools that have increased the demand for robust security measures to prevent unauthorized access, disclosure, or alteration of protected health information. Data integration and artificial intelligence (AI) have had an impact on the development of data security standards in the United States' digital public health system, which is the focus of this inquiry. By doing an empirical investigation, it can determine how AI-driven technology can automate incident response, compliance monitoring, and threat identification. Data security issues caused by data silos and broken infrastructure are also discussed, as is research into the possibility of data system integration as a solution. The analysis of case studies, healthcare IT professional survey data, and public breach reports uncovered important advantages, disadvantages, and recommended procedures. We hope that policymakers, healthcare organizations, and tech developers will find our findings useful as they work to create a digital health environment that is secure, reliable, and equitable for everyone. In addition, the paper stresses the need to eliminate biases in AI and reduce resource disparities for smaller businesses. It lays the framework for further efforts to secure electronic public health records. Keywords - AI-driven fraud detection, Healthcare data privacy and Data integrity, Federated Privacy learning, Blockchain Compliance regulations, Privacy-preserving AI.