AI 中文总结
针对医院AI部署孤立、规模化难的问题，提出含智能体编排、合规策略、隐私保护数据层的多层架构，通过原型验证可减少任务耗时与文档工作量，为医院AI平台建设提供实用蓝图。
AI 中文摘要
医院正在快速将人工智能应用于分诊、影像、排班等场景，但多数部署仍为孤立的单点解决方案，被锁定在部门竖井中，导致重复工作、隐藏风险及企业价值未实现。尽管医疗AI市场爆炸式增长且投资加速，但据估计70%-80%的医疗AI试点未能规模化，主要原因是治理缺口、数据碎片化及缺失的集成蓝图。本研究提出一种医院专用的、合规优先的智能体AI架构，包含多个可互操作的层，对现有医院AI平台模型进行了扩展：（i）智能体编排层，用于跨临床、运营和财务领域的多智能体工作流；（ii）合规与策略层，将HIPAA、GDPR、欧盟AI法案、DISHA法案、印度DPDP法案及ISO/IEC安全与隐私标准的策略即代码进行集中管理；（iii）隐私保护数据架构，将联邦学习、差分隐私和安全 enclaves 接入真实世界的医院信息管理系统（HIMS）流程。本研究使用结构真实的合成医院数据集及可部署的开源原型实现，展示了分诊风险预测、工作流优化和合规日志记录的端到端编排，在保持策略保护的数据访问的同时，实现了任务周转时间和手动文档工作的大幅模拟减少。该架构为医院管理者提供了从临时工具转向受治理、全球合规、关注ROI的AI平台的实用蓝图，可针对本地部署、混合部署和云原生部署进行定制。
英文摘要
Hospitals are rapidly adopting artificial intelligence for triage, imaging, scheduling etc., yet most deployments remain isolated point solutions locked inside departmental silos, resulting in duplicated effort, hidden risks, and unrealized enterprise value. Despite explosive growth of AI in healthcare market and accelerating investment, an estimated 70-80% of healthcare AI pilots fail to scale, largely due to governance gaps, fragmented data, and missing integration blueprints. This research proposes a hospital-specific, compliance-first, Agentic AI architecture with multiple interoperable layers, extending existing hospital AI platform models with: (i) an Agent Orchestration Layer for multi-agent workflows across clinical, operational, and financial domains, (ii) a Compliance and Policy Layer that centralizes policy-as-code for HIPAA, GDPR, the EU AI Act, DISHA Act, India's DPDP Act, and ISO/IEC security and safety standards, and (iii) a Privacy-Preserving Data Fabric that plugs federated learning, differential privacy, and secure enclaves into real-world Hospital Information Management System (HIMS) flows. Using a synthetic but structurally realistic hospital dataset and an open, ready-to-deploy prototype implementation, this study demonstrates the end-to-end orchestration of triage risk prediction, workflow optimization, and compliance logging, achieving substantial simulated reductions in task turnaround times and manual documentation effort while maintaining policy-guarded data access. The resulting architecture offers hospital leaders a pragmatic blueprint to move from ad hoc tools to a governed, globally compliant, ROI-focused AI platform that can be tailored to on-premise, hybrid and cloud-native deployments.
CommentsPeer-reviewed published article
Journal refIJISRT, 11-2026(5), IJISRT26MAY1651
