Evidence map›Paper›PMID 41023287›Full record

ArticleLa Radiologia medica2025

Readability versus accuracy in LLM-transformed radiology reports: stakeholder preferences across reading grade levels.

Hong-Seon Lee, Sungjun Kim, Songsoo Kim, Jeongrok Seo, Won Hwa Kim, Jaeil Kim, Kyunghwa Han, Shin Hye Hwang, Young Han Lee

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Article in La Radiologia medica, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Hong-Seon LeeDepartment of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.
Sungjun KimDepartment of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.
Songsoo KimDepartment of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea.
Jeongrok SeoSchool of Computer Science and Engineering, Kyungpook National University, Daegu, Republic of Korea.
Won Hwa KimBeamWorks Inc., Daegu, Republic of Korea.
Jaeil KimSchool of Computer Science and Engineering, Kyungpook National University, Daegu, Republic of Korea.
Kyunghwa HanDepartment of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.
Shin Hye HwangDepartment of Radiology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Republic of Korea.
Young Han LeeInstitute for Innovation in Digital Healthcare, Yonsei University, Seoul, Republic of Korea. radiologie@gmail.com.ORCID http://orcid.org/0000-0002-5602-391X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo examine how reading grade levels affect stakeholder preferences based on a trade-off between accuracy and readability. MATERIAL AND

methodsA retrospective study of 500 radiology reports from academic and community hospitals across five imaging modalities was conducted. Reports were transformed into 11 reading grade levels (7-17) using Gemini. Accuracy, readability, and preference were rated on a 5-point scale by radiologists, physicians, and laypersons. Errors (generalizations, omissions, hallucinations) and potential changes in patient management (PCPM) were identified. Ordinal logistic regression analyzed preference predictors, and weighted kappa measured interobserver reliability.

resultsPreferences varied across reading grade levels depending on stakeholder group, modality, and clinical setting. Overall, preferences peaked at grade 16, but declined at grade 17, particularly among laypersons. Lower reading grades improved readability but increased errors, while higher grades improved accuracy but reduced readability. In multivariable analysis, accuracy was the strongest predictor of preference for all groups (OR: 30.29, 33.05, and 2.16; p <0 .001), followed by readability (OR: 2.73, 1.70, 2.01; p <0.001).

conclusionHigher-grade levels were generally preferred due to better accuracy, with a range of 12-17. Further increasing grade levels reduced readability sharply, limiting preference. These findings highlight the limitations of unsupervised LLM transformations and suggest the need for hybrid approaches that maintain original reports while incorporating explanatory content to balance accuracy and readability.

Indexed as

ComprehensionRadiologyFemaleHumansReproducibility of ResultsRetrospective StudiesArtificial intelligence in radiologyLarge language models (LLMs)Patient-centered communicationRadiology reportsReadability and accuracy

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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.