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TRV-2026-1041Certified recordPeer-reviewed

A Quality Assessment Rubric for Artificial Intelligence-Generated Patient-Friendly Radiology Reports

Background: Artificial intelligence (AI) tools are being used to translate radiology reports into plain language, but translation errors may compromise comprehension and safety. Objective: To develop and evaluate a rubric for assessing the quality and safety of AI-generated patient-friendly radiology reports. Methods: In this prospective study (conducted from February 2025 to December 2025), survey-workshop cycles, involving lay participants and a multidisciplinary panel, were used to develop a rubric for gradin…

Health · The Trace — both readings · certified 2026-09-10 · v1 · article view · machine-readable

Current reading — gain

A five-attribute rubric for AI-generated patient-friendly radiology reports showed almost-perfect agreement between lay and radiologist team members and may provide a standardized safeguard before patient distribution.

Current reading — problem

AI tools translating radiology reports into plain language can produce translation errors that compromise comprehension and safety, causing reports to be graded unsafe and warrant withholding from patients.

What this doesn’t fix

Authors note rubric requires further training and validation before clinical use, and wider field testing showed only moderate agreement with reference standards.

Evidence

Reader signal

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Truvace Impact Record TRV-2026-1041, v1: “A Quality Assessment Rubric for Artificial Intelligence-Generated Patient-Friendly Radiology Reports.” Truvace, 2026-09-10. /record/TRV-2026-1041 (accessed at citation time). sha256 63a875426e2aeed1…

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