Adaptive AI Testing for Regional Compliance in eHealth Platforms

Abstract

Ensuring regional regulatory compliance in eHealth platforms is critical for safeguarding patient data privacy, security, and interoperability across diverse jurisdictions. This paper presents an adaptive AI-driven testing framework designed to dynamically assess and validate eHealth software against region-specific compliance requirements, such as GDPR, HIPAA, and other local regulations. Leveraging natural language processing to interpret evolving regulatory texts and reinforcement learning to optimize test strategies, the proposed framework continuously adapts test cases in response to changes in compliance standards and software updates. Experimental evaluation on multiple eHealth platforms demonstrates that the AI-driven approach improves compliance coverage, reduces manual testing efforts, and accelerates the detection of compliance violations. This work highlights the potential of adaptive AI testing to enhance regulatory adherence and trustworthiness of eHealth systems operating in complex legal landscapes.