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

DC Field Value Language
dc.contributor.authorLee, Hong-Seon-
dc.contributor.authorKim, Sungjun-
dc.contributor.authorKim, Songsoo-
dc.contributor.authorSeo, Jeongrok-
dc.contributor.authorKim, Won Hwa-
dc.contributor.authorKim, Jaeil-
dc.contributor.authorHan, Kyunghwa-
dc.contributor.authorHwang, Shin Hye-
dc.contributor.authorLee, Young Han-
dc.date.accessioned2026-01-16T05:56:24Z-
dc.date.available2026-01-16T05:56:24Z-
dc.date.created2026-01-02-
dc.date.issued2025-12-
dc.identifier.issn0033-8362-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/209792-
dc.description.abstractPurpose: To examine how reading grade levels affect stakeholder preferences based on a trade-off between accuracy and readability. Material and methods: A 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. Results: Preferences 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). Conclusion: Higher-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.-
dc.languageEnglish-
dc.publisherSpringer-
dc.relation.isPartOfRADIOLOGIA MEDICA-
dc.relation.isPartOfRADIOLOGIA MEDICA-
dc.subject.MESHComprehension*-
dc.subject.MESHFemale-
dc.subject.MESHHumans-
dc.subject.MESHRadiology*-
dc.subject.MESHReproducibility of Results-
dc.subject.MESHRetrospective Studies-
dc.titleReadability versus accuracy in LLM-transformed radiology reports: stakeholder preferences across reading grade levels-
dc.typeArticle-
dc.contributor.googleauthorLee, Hong-Seon-
dc.contributor.googleauthorKim, Sungjun-
dc.contributor.googleauthorKim, Songsoo-
dc.contributor.googleauthorSeo, Jeongrok-
dc.contributor.googleauthorKim, Won Hwa-
dc.contributor.googleauthorKim, Jaeil-
dc.contributor.googleauthorHan, Kyunghwa-
dc.contributor.googleauthorHwang, Shin Hye-
dc.contributor.googleauthorLee, Young Han-
dc.identifier.doi10.1007/s11547-025-02098-5-
dc.relation.journalcodeJ02594-
dc.identifier.eissn1826-6983-
dc.identifier.pmid41023287-
dc.identifier.urlhttps://link.springer.com/article/10.1007/s11547-025-02098-5-
dc.subject.keywordLarge language models (LLMs)-
dc.subject.keywordRadiology reports-
dc.subject.keywordReadability and accuracy-
dc.subject.keywordArtificial intelligence in radiology-
dc.subject.keywordPatient-centered communication-
dc.contributor.affiliatedAuthorLee, Hong-Seon-
dc.contributor.affiliatedAuthorKim, Sungjun-
dc.contributor.affiliatedAuthorKim, Songsoo-
dc.contributor.affiliatedAuthorHan, Kyunghwa-
dc.contributor.affiliatedAuthorHwang, Shin Hye-
dc.contributor.affiliatedAuthorLee, Young Han-
dc.identifier.scopusid2-s2.0-105018204615-
dc.identifier.wosid001584241100001-
dc.citation.volume130-
dc.citation.number12-
dc.citation.startPage1986-
dc.citation.endPage1999-
dc.identifier.bibliographicCitationRADIOLOGIA MEDICA, Vol.130(12) : 1986-1999, 2025-12-
dc.identifier.rimsid90708-
dc.type.rimsART-
dc.description.journalClass1-
dc.description.journalClass1-
dc.subject.keywordAuthorLarge language models (LLMs)-
dc.subject.keywordAuthorRadiology reports-
dc.subject.keywordAuthorReadability and accuracy-
dc.subject.keywordAuthorArtificial intelligence in radiology-
dc.subject.keywordAuthorPatient-centered communication-
dc.subject.keywordPlusENGAGEMENT-
dc.subject.keywordPlusCARE-
dc.type.docTypeArticle; Early Access-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalWebOfScienceCategoryRadiology, Nuclear Medicine & Medical Imaging-
dc.relation.journalResearchAreaRadiology, Nuclear Medicine & Medical Imaging-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Biomedical Systems Informatics (의생명시스템정보학교실) > 1. Journal Papers

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