Evidence map›Paper›PMID 42658364›Full record

ArticleInsights into imaging2026

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

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

Abstract read
In one paragraph

Article in Insights into imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Agreen HorrDepartment of Radiology and Neuroradiology, University Medical Center Schleswig-Holstein, Kiel, Germany. agreen.horr@uksh.de.ORCID http://orcid.org/0009-0008-7315-4779
Isabella Luisa WaltherDepartment of Radiology and Neuroradiology, University Medical Center Schleswig-Holstein, Kiel, Germany.
Alexandra BonietzkiDepartment of Vascular and Endovascular Surgery, University Medical Center Schleswig-Holstein, Kiel, Germany.
Tim-Christian PieschDepartment of Radiology and Neuroradiology, University Medical Center Schleswig-Holstein, Kiel, Germany.
Sönke PetersDepartment of Radiology and Neuroradiology, University Medical Center Schleswig-Holstein, Kiel, Germany.
Mehmet Mert SarikayaDepartment of Radiology and Neuroradiology, University Medical Center Schleswig-Holstein, Kiel, Germany.
Svea SeehaferDepartment of Radiology and Neuroradiology, University Medical Center Schleswig-Holstein, Kiel, Germany.
Olav JansenDepartment of Radiology and Neuroradiology, University Medical Center Schleswig-Holstein, Kiel, Germany.
Patrick LangguthDepartment of Radiology and Neuroradiology, University Medical Center Schleswig-Holstein, Kiel, Germany.
Schekeb AludinDepartment of Radiology and Neuroradiology, University Medical Center Schleswig-Holstein, Kiel, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesRadiology 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

methodsIn 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.

resultsParticipants 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.

conclusionIn 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.

Indexed as

Generative artificial intelligenceHealth LiteracyLarge language modelsPatient-centered careRadiology

Identifiers

PMID42658364
PMCPMC13522326

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.