Trustworthy AI and Machine Learning for Patient Analytics

This white paper presents a comprehensive approach to Artificial Intelligence (AI) and Machine Learning (ML) designed for patient analytics and research. It outlines a novel privacy-first and AI-secure architecture developed by IQVIA, which integrates synthetic data abstractions, federated learning, and AI Governance and Privacy Operations (AI PrivOps) monitoring. The architecture aims to ensure safe and effective AI/ML applications while maintaining confidentiality and minimizing risks associated with data exposure. The paper discusses the importance of balancing data utility with rigorous protection in healthcare, emphasizing the need for robust de-identification methods and standardized practices. It also highlights the regulatory landscape affecting AI and data protection, advocating for systems that are secure by design and capable of adapting to evolving oversight requirements. The proposed framework is aligned with global standards and aims to set a new benchmark for ethical AI innovation in healthcare, ensuring accountability and transparency in the use of sensitive health data.