从孤岛算法到合规优先的智能体平台：医院 AI 系统的多层架构
From Siloed Algorithms to Compliance-First Agentic Platforms: A Multi-Layered Architecture for Hospital AI Systems
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一篇研究提出面向医院的合规优先 Agentic AI 多层架构，包含 Agent Orchestration Layer、集中 HIPAA、GDPR、EU AI Act 等政策的 Compliance and Policy Layer，以及结合联邦学习、差分隐私的 Privacy-Preserving Data Fabric。
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Abstract: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.
| Comments: | arXiv admin note: This submission has been withdrawn because it does not meet arXiv's research content quality standards |
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Multiagent Systems (cs.MA) |
| Cite as: | arXiv:2608.06112 [cs.AI] |
| (or arXiv:2608.06112v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2608.06112 arXiv-issued DOI via DataCite |
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| Journal reference: | IJISRT, 11-2026(5), IJISRT26MAY1651 |
| Related DOI: | https://doi.org/10.38124/ijisrt/26May1651
DOI(s) linking to related resources |
Submission history
From: arXiv Admin [view email]
[v1]
Thu, 6 Aug 2026 14:44:31 UTC (3,510 KB)
[v2]
Thu, 5 Nov 2026 18:16:22 UTC (1 KB) (withdrawn)
来源：arXiv cs.AI · arxiv.org