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在医疗保健领域构建可防御的人工智能平台

医药生物2025-07-28艾昆纬L***

AI智能总结

healthcare organizations face challenges in adopting AI while ensuring data protection and regulatory compliance. The report introduces the concept of "defensible AI," defined as AI systems that reliably achieve organizational objectives while meeting rigorous safety, data protection, and regulatory standards. It emphasizes three core concepts for building defensible AI platforms:

Control Management: This involves systematically aligning AI functionalities with regulatory requirements and best practices through controls and requirements mapping. The process includes defining necessary controls, translating them into technical specifications, and creating operating guidelines for continuous monitoring and adaptation. An example is using LLMs and NLP to automate control mapping from regulatory documents to frameworks like ISO and NIST, achieving efficient risk assessment and implementation planning.
Secure Insights with Synthetic Trends: To balance AI utility and data security, the report proposes using "synthetic trends" derived from abstracted data within embedding spaces. This method preserves statistical properties and behavioral patterns for AI modeling without exposing sensitive data. By training models on synthetic trends, organizations can enhance data utility while ensuring privacy and compliance. The report highlights a case study where synthetic health trends increased machine learning precision by over 16x, unlocking revenue growth potential 10x greater than previous methods, without altering baseline approaches or feature engineering.
Secure Health Fabric: The report advocates for a data fabric architecture aligned with a data mesh strategy, creating segregated workspaces controlled by independent teams. This setup allows for decentralized innovation while maintaining centralized oversight through protocols, AI agents, and human-in-the-loop checkpoints. The secure health fabric ensures data integrity and security, supports plug-and-play functionality for diverse AI use cases, and incorporates federated learning (both horizontal and vertical) to combine insights across datasets without compromising confidentiality.

The report concludes by emphasizing the importance of scalable AI management, robust data security, and thoughtful data architecture grounded in rigorous controls and standards. It recommends adopting these principles to lead in the evolving healthcare AI landscape, ensuring innovation aligns with patient care, operational efficiency, and trust. The IQVIA Synthetic Trends Engine and AI Governance and Privacy Operations (AI PrivOps) monitoring are presented as tools to support privacy and security in AI applications.

Constructing Defensible AIPlatforms in Healthcare How we unlocked revenue growth potential exceeding10x what was previously possible Table of contents What does it take to manage AI at scale?3Controls mapping with AI4How can we secure AI models without limiting utility?5Reconstruction risk7Is your platform ready for AI governance?8Case Study10Final thoughts11To learn more11References12About the author13About IQVIA Applied AI Science (AAIS)13 Healthcare organizations face increasing pressure to adopt advancedtechnologies while navigating complex Artificial Intelligence (AI), dataprotection, and regulatory expectations. Based on our experience supportingboth public and private institutions, we offer a defensible approach to AIplatforms that delivers meaningful insights while safeguarding patient trust.As we delve deeper into digital transformation, the significance of robust andsecure data and AI platforms becomes clear. Such platforms are fundamentalin harnessing the full potential of health data, ensuring that innovations lead toimproved health outcomes and efficiency gains while advancing standards ofdata integrity and protection. So, we ask ourselves: How do we ensure thattechnical design decisionssupport regulatorydefensibility andpatient expectations? Are current governancemodels ready tomanage the risks posedby generative AI andautonomous systems? How can healthcareorganizations adopt AIwithout compromisingtrust or data protection? What makes an AIsystem truly defensible,beyond compliancechecklists? In 2024, we introduceddefensible AIas data and AIsystems that reliably achieve organizational objectivesin a manner that meets or exceeds rigorous safety,data protection, and regulatory standards. It involvesan approach where technology drives the adoption ofAI safety and AI security standards. This is crucial inthe healthcare sector, where data is confidential andsensitive, and the implications of mishandling data canbe significant. By building platforms that are powerfulwhile being trustworthy and transparent, we pave theway for advancements that are both innovative andsecure, ensuring that patient welfare remains at theforefront of technological progress. In this brief, we consider three core concepts todefensible AI that have emerged from our work withclients and the development of an AI platform that isachieving remarkable success. These insights are drawnfrom our work across healthcare sectors and are sharedto help other organizations implement defensible AIsolutions aligned with emerging standards. trendsto enhance machine learning models withoutcompromising the security of the data used. Effective platform design: The architecturethat securely bridges health and other data to Scalable AI management: Central to these effortsis the concept ofcontrol management, which involves create meaningful insights plays a criticalrole in driving business outcomes. Akey consideration is finding the right mapping controls and requirements againstbest practice standards and functional needs— and continuously monitoring those controls balance between central control (a data fabric) anddecentralized autonomy (a data mesh). In working withsensitive data, the architecture of many platformswill involve segregated workspaces controlled byindependent teams, each producing their own dataproducts while maintaining rigorous governanceindependently. This structure boosts innovationand efficiency while reinforcing the security andcompliance of the data and deployed AI system. when operating the platform. This ensures that everycomponent of the AI system performs optimallyand operates within a framework that is alignedwith regulatory and patient expectations. It’s also aproactive approach that ensures ongoing alignmentand responsiveness to emerging challenges andtechnological advancement. Secure insights: AI security is introducing newchallenges to contend with, beyond considerations As we explore these elements further, we willdelve into the specifics of control management,the innovative applications of synthetic trends inmachine learning, and the strategic importance ofasecurehealth fabricin sustaining the integrity anddefensibility of AI platforms. Our experience hasshown that each component is integral to shapinga healthcare environment where technology andpatient-centric care converge seamlessly. of the data that is accessed, and by whom.Emerging data protection technologiesprovide a means of ensuring only the right data, at the right time, and for the right purposes areavailable. But securing the data, or analytical toolsused to process that data, requires new thinking toaddress a rapidly modernizing technology landscape.We consider the balance between data and AIsecurity, introducing a technique we callsynthetic What does it take to manage AI at scale? Control management, at its core, involves thesystematic alignment of AI functionalities withregulatory requirements and best practices througha proce

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