Perceived communication quality of a standardized neuroradiology report following AI-based simplification: a randomized, blinded study

OBJECTIVES: Radiology reports are primarily written for professional communication and may be difficult for patients to understand. We investigated whether AI-based simplification of a single standardized neuroradiology report improves participant-rated communication quality compared with a conventional professional-language report. MATERIALS AND METHODS: In this prospective, randomized, blinded study, 100 adults received either an AI-simplified or a conventional report based on the same standardized neuroradiology case. Two board-certified radiologists reviewed the simplified version before evaluation. Participants rated clarity and structure, tone and trust, usability, and overall impression on structured 5-point scales. No objective comprehension test was performed. Group differences were assessed using Welch's t-tests, with effect sizes reported as Cohen's d; Mann-Whitney U-tests served as sensitivity analyses. Open-ended responses were analyzed qualitatively. RESULTS: Participants rated the AI-simplified report more favorably across all domains (all p < 0.001). Effect sizes were large to very large, ranging from d = 1.31 for Tone and Trust to d = 2.10 for Overall Impression. Nonparametric sensitivity analyses confirmed all group differences. Qualitative responses likewise emphasized clearer explanations, simpler language, and improved structure. CONCLUSION: In this single standardized neuroradiology case, AI-based simplification improved participant-rated communication quality. Studies using multiple cases and outputs should determine whether such perceived improvements translate into objective comprehension, recall, decision-making, and clinically meaningful communication benefits. KEY POINTS: Question Can radiologist-reviewed AI simplification address the communication gap by improving perceived communication quality of a standardized neuroradiology report compared with a conventional professional-language report? Findings In this randomized study with concealed AI involvement, the AI-simplified report received higher ratings across all four participant-rated communication domains. Critical relevance statement In a standardized neuroradiology report, AI-based simplification improved perceived communication quality, supporting further evaluation of patient-facing report explanations as adjuncts to conventional reports with explicit quality-assurance criteria.

Authors

Institutions

Publication Details

Journal
Insights into Imaging
Published
2026-08-27
DOI
https://doi.org/10.1186/s13244-026-02391-5
Primary Topic
Radiology practices and education
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Perceived communication quality of a standardized neuroradiology report following AI-based simplification: a randomized, blinded study

Tim-Christian Piesch, Sönke Peters, Olav Jansen, Patrick Langguth et al.
Insights into Imaging
Radiology practices and education
article

Perceived communication quality of a standardized neuroradiology report following AI-based simplification: a randomized, blinded study

Tim-Christian Piesch, Sönke Peters, Olav Jansen, Patrick Langguth, Agreen Horr, Schekeb Aludin, Svea Seehafer, Alexandra Bonietzki, Mehmet Mert Sarikaya, Isabella Luisa Walther
article en

Abstract

OBJECTIVES: Radiology reports are primarily written for professional communication and may be difficult for patients to understand. We investigated whether AI-based simplification of a single standardized neuroradiology report improves participant-rated communication quality compared with a conventional professional-language report. MATERIALS AND METHODS: In this prospective, randomized, blinded study, 100 adults received either an AI-simplified or a conventional report based on the same standardized neuroradiology case. Two board-certified radiologists reviewed the simplified version before evaluation. Participants rated clarity and structure, tone and trust, usability, and overall impression on structured 5-point scales. No objective comprehension test was performed. Group differences were assessed using Welch's t-tests, with effect sizes reported as Cohen's d; Mann-Whitney U-tests served as sensitivity analyses. Open-ended responses were analyzed qualitatively. RESULTS: Participants rated the AI-simplified report more favorably across all domains (all p < 0.001). Effect sizes were large to very large, ranging from d = 1.31 for Tone and Trust to d = 2.10 for Overall Impression. Nonparametric sensitivity analyses confirmed all group differences. Qualitative responses likewise emphasized clearer explanations, simpler language, and improved structure. CONCLUSION: In this single standardized neuroradiology case, AI-based simplification improved participant-rated communication quality. Studies using multiple cases and outputs should determine whether such perceived improvements translate into objective comprehension, recall, decision-making, and clinically meaningful communication benefits. KEY POINTS: Question Can radiologist-reviewed AI simplification address the communication gap by improving perceived communication quality of a standardized neuroradiology report compared with a conventional professional-language report? Findings In this randomized study with concealed AI involvement, the AI-simplified report received higher ratings across all four participant-rated communication domains. Critical relevance statement In a standardized neuroradiology report, AI-based simplification improved perceived communication quality, supporting further evaluation of patient-facing report explanations as adjuncts to conventional reports with explicit quality-assurance criteria.

Insights into ImagingVol. 17(1)
University Hospital Schleswig-Holstein (DE), University of Lübeck (DE)
Peace, Justice and strong institutions
Openalex Percentile: Top 11%
Radiology practices and education
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.