Radiology reports remain the primary mechanism by which imaging findings are communicated to clinical teams. However, much of the structured information behind these reports, including measurements, image evidence, prior comparisons, lesion identity, uncertainty, and terminology, often remains trapped in free text or fragmented across picture archiving and communication systems, radiology information systems, reporting workstations, worksheets, advanced visualization tools, and electronic health records. This paper proposes a human-supervised, evidence-linked reference architecture for structured radiology reporting. The framework combines exam-specific templates, speech-to-structure processing, measurement and segmentation capture, controlled AI-assisted drafting, and standards-based interoperability using DICOM, DICOM Structured Reporting, DICOM Segmentation, HL7 FHIR, RadLex, SNOMED CT, LOINC, and UCUM. The system is positioned not as an autonomous report generator, but as a structured intelligence layer for enterprise imaging that supports reviewed reporting, longitudinal comparison, clinical data reuse, governance, and integration with PACS, RIS, EHR, analytics, and registry workflows. The paper also discusses modality-specific deployment considerations, clinical safety risks, validation requirements, cybersecurity, privacy, quality management, and regulatory boundaries for AI-assisted radiology reporting systems.

翻译：放射学报告仍然是影像学发现向临床团队传递的主要机制。然而，这些报告背后的大量结构化信息（包括测量值、影像证据、既往对比、病灶识别、不确定性及术语）常常被困在自由文本中，或分散在影像归档与通信系统、放射信息系统、报告工作站、工作表、高级可视化工具及电子健康记录中。本文提出了一种人机协同、证据关联的结构化放射学报告参考架构。该框架整合了检查专用模板、语音到结构化处理、测量与分割捕获、受控的AI辅助撰写，以及基于DICOM、DICOM结构化报告、DICOM分割、HL7 FHIR、RadLex、SNOMED CT、LOINC和UCUM的标准互操作性。该系统并非定位于自主报告生成器，而是作为企业影像的结构化智能层，支持审核报告、纵向对比、临床数据复用、治理，以及与PACS、RIS、EHR、分析系统和注册工作流的集成。本文还讨论了AI辅助放射学报告系统在模态特定部署考量、临床安全风险、验证要求、网络安全、隐私、质量管理及监管边界方面的内容。