Trustworthy AI and ML for Patient Analytics

This white paper presents an innovative approach to Artificial Intelligence (AI) and Machine Learning (ML) focused on patient analytics and research. It outlines a privacy-first and AI-secure architecture developed by IQVIA that addresses contemporary data protection concerns while enabling effective AI/ML applications. The paper emphasizes the importance of balancing data utility with rigorous protection, particularly in healthcare, where sensitive health data is involved. It introduces foundational concepts such as synthetic data abstraction, federated learning, and integrated AI governance and privacy operations (AI PrivOps). These strategies aim to minimize risks associated with AI/ML, including data exposure and unauthorized access. The document also discusses the evolving regulatory landscape and the necessity for AI systems that are secure by design. By leveraging synthetic trends and continuous monitoring, IQVIA's approach seeks to set a new standard for ethical and responsible use of AI in healthcare, ensuring compliance with global standards and addressing the challenges posed by emerging technologies.