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New architecture links radiology report findings to evidence

Researchers have proposed a new reference architecture for structured radiology reporting that links evidence directly to report content. This human-supervised system aims to extract and organize structured information, such as measurements and lesion identities, which are often lost in free text or fragmented across various systems. The framework integrates exam-specific templates, AI-assisted drafting, and interoperability standards to support reviewed reporting, longitudinal comparison, and integration with enterprise imaging workflows. AI

影响 This architecture could improve the accuracy and reusability of clinical data by linking imaging findings to their evidence, potentially enhancing AI-driven diagnostics and research.

排序理由 The cluster contains an academic paper detailing a proposed architecture for structured radiology reporting. [lever_c_demoted from research: ic=1 ai=1.0]

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  1. arXiv cs.AI TIER_1 English(EN) · Houman Kazemzadeh, Kamyar Naderi ·

    Evidence-Linked Radiology Reporting: A Human-Supervised Reference Architecture for Structured Imaging Intelligence

    arXiv:2605.25120v1 Announce Type: cross Abstract: 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 compariso…