可信人工智能/机器学习用于患者分析和研究

Trustworthy AI/ML for PatientAnalytics and Research AI-secure, privacy-first, with continuous monitoring and oversight Table of contents Responsible innovation in patient analytics and research2Heightened care in AI/ML2Adopting a principled, AI-secure approach to AI/ML2Shifting baseline for AI-Secure AI/ML3Bridging AI and data protection with federated modeling3Collection limitation and data minimization3Use limitation and purpose specification4Security safeguards4Accountability and oversight4Openness and transparency4Federated learning for AI/ML5Understanding the data journey for federated learning6Source ingestion: pseudonymization andsegregation6Horizontal federated learning: generating synthetic trends6Vertical federated learning: AI-secure AI/ML7Safe outputs7Beyond de-identification: managing reconstruction risk8Leveraging synthetic trends for AI/ML8Managing reconstruction risk9AI governance and privacy operations (AI PrivOps): an integrated governance function10Continuous monitoring of AI PrivOPs metrics10Oversight without exposure11Human-in-the-loop for accountability11Ethics Board for patient analytics andresearch12Conclusion13Acknowledgment13 This paper outlines our approach to Artificial Intelligence (AI) and MachineLearning (ML) that withstands today’s threat landscape and serves as a blueprintfor sustainable innovation. It’s how we raise the bar for defensible AI inhealthcare applications and beyond, representing a shift from traditional datapractices to AI-security as a design feature. Adopting a principled, AI-secure approach to AI/ML Responsible innovation inpatient analytics and research Life sciences are being transformed by ArtificialIntelligence (AI) and Machine Learning (ML). But withthat transformation comes a critical question: how dowe unlock value from sensitive health data withoutundermining trust, transparency, or control? Traditionalsafeguards are no longer enough in an era of AI/ML,where subtle patterns can be used — or misused — inunanticipated ways. The stakes are especially high inhealthcare, where data utility must be balanced withrigorous protection. Envision a future where AI/ML models for health andwellness applications are proactively engineered withresilience and security at every layer. Sensitive dataremains protected, systemic vulnerabilities and risks aremanaged before they surface, and insights are extractedand utilized without exposure. This is the new frontierof AI and data protection, where the architecture ispurpose built for robustness, trust, and availabilitywithout compromising analytical power. This white paper introduces a novel, principled approachthat puts AI security at the center of the systemarchitecture. Powered by theIQVIA Synthetic Trends Engine,our approach is grounded on three foundational pillars:synthetic data abstraction, federated learning architecture,and integrated AI governance and privacyoperations. Robust de-identification methods, which removeidentifying elements, can be used but the industry lackswidespread adoption of standardized practices. Thisabsence of fixed standards provides space to exploreforward-looking approaches, especially in light ofemerging AI/ML threats that will need to be addressed.As AI/ML and other developing technologies reshape thelandscape, more sophisticated strategies are needed tobalance AI/ML and data protection with responsible use. Synthetic data abstraction: Traditional modelsrely on raw data, increasing the surface areafor risk. Instead of relying on raw data, ourapproach transforms high-dimensional signalsinto non-reversible trend vectors using AI-securedimensionality reduction techniques. Synthetictrends capture useful patterns to maintainanalytical utility while minimizing downstreamreconstruction risk by design, approaching near-zero exposure. This enables inferential bridging foranalytics across isolated datasets. Heightened care in AI/ML This whitepaper introduces a novel privacy-first andAI-secure architecture for defensible AI developed byIQVIA. In response to AI and data protection concerns,the platform combines synthetic data abstractions,federated learning, and integrated AI Governance andPrivacy Operations (AI PrivOps) monitoring to enable safe,effective AI/ML without compromising confidentiality. Federated learning architecture: Rather thanaggregating data into a central repository,our system employs a federated architecturein which source data are segregated withinsecure environments. Raw data never leavesits origin, and only synthetic trends, which arethemselves AI-secure, are combined for modeling.Decentralized computation ensures that datasovereignty is respected, significantly reducing therisk of exposure and unauthorized access, whilestill allowing for collaborative analytics. The solution enforces AI and data protection througharchitectural features such as input transformation, non-reversibility, and latent space modeling. Aligned withglobal standards such as ISO/IEC 4200