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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2025-06-06-IQVIA杨***

AI智能总结

healthcare organizations can adopt AI without compromising trust or data protection by building defensible AI platforms. This involves three core concepts: scalable AI management, secure insights, and effective platform design.

Scalable AI management focuses on control management, which includes mapping controls and requirements against best practice standards and functional needs, and continuously monitoring those controls. This ensures AI systems perform optimally within a framework aligned with regulatory and patient expectations. Key steps include identifying necessary controls, translating them into technical specifications, and establishing operating guidelines for ongoing management.

Secure insights address the challenge of balancing AI utility with data security. The approach uses synthetic trends within abstracted data spaces to enhance machine learning models without compromising data security. This method preserves data utility for AI while protecting sensitive information, significantly reducing reconstruction risk.

Effective platform design involves creating a balance between central control and decentralized innovation. A "secure health fabric" architecture, combining data mesh and data fabric principles, allows independent teams to develop and manage their own AI and data products within segregated workspaces, each governed by rigorous protocols and human-in-the-loop checkpoints. AI agents operate within predefined guardrails to automate tasks while preserving oversight.

A case study demonstrates how synthetic health trends increased machine learning precision by over 16x in a federated architecture, unlocking revenue growth potential 10x greater than previously possible. The platform uses a hybrid model of federated learning, combining synthetic trends across different functional areas under strict security controls.

Key conclusions emphasize the importance of embedding defensibility into data systems. By adopting scalable AI management, robust data security practices, and thoughtfully designed data architectures, healthcare organizations can drive innovation while upholding patient care and trust. The IQVIA Synthetic Trends Engine and secure health fabric provide tools for implementing these principles, ensuring responsible AI use aligned with privacy and security standards.

Table of contentsWhat does it take to manage AI at scale?Controls mapping with AIHow can we secure AI models without limiting utility?Reconstruction riskIs your platform ready for AI governance?Case StudyFinal thoughtsTo learn moreReferencesAbout the authorAbout IQVIA Applied AI Science (AAIS) 34578101111121313 iqvia.com | 1Healthcare 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 can healthcareorganizations adopt AIwithout compromisingtrust or data protection?What makes an AIsystem truly defensible,beyond compliancechecklists?Are current governancemodels ready tomanage the risks posedby generative AI andautonomous systems?How do we ensure thattechnical design decisionssupport regulatorydefensibility andpatient expectations? 2 | Constructing Defensible AI Platforms in HealthcareIn 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.Scalable AI management: Central to these effortsis the concept ofcontrol management, which involvesmapping controls and requirements againstbest practice standards and functional needs— and continuously monitoring those controlswhen 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 considerationsof the data that is accessed, and by whom.Emerging data protection technologiesprovide a means of ensuring only the rightdata, 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 trendsto enhance machine learning models withoutcompromising the security of the data used.Effective platform design: The architecturethat securely bridges health and other data tocreate meaningful insights plays a criticalrole in driving business outcomes. Akey consideration is finding the rightbalance 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.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. What does it take to manage AI at scale?We have seen time and again that in the realm ofhealthcare technology, the integration of AI presentstransformative potential — from enhancing diagnosticaccuracy to personalizing patient care. However,the inherent complexities and the sensitive natu

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