Cited 0 times in 
Cited 0 times in 
Readability versus accuracy in LLM-transformed radiology reports: stakeholder preferences across reading grade levels
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Lee, Hong-Seon | - |
| dc.contributor.author | Kim, Sungjun | - |
| dc.contributor.author | Kim, Songsoo | - |
| dc.contributor.author | Seo, Jeongrok | - |
| dc.contributor.author | Kim, Won Hwa | - |
| dc.contributor.author | Kim, Jaeil | - |
| dc.contributor.author | Han, Kyunghwa | - |
| dc.contributor.author | Hwang, Shin Hye | - |
| dc.contributor.author | Lee, Young Han | - |
| dc.date.accessioned | 2026-01-16T05:56:24Z | - |
| dc.date.available | 2026-01-16T05:56:24Z | - |
| dc.date.created | 2026-01-02 | - |
| dc.date.issued | 2025-12 | - |
| dc.identifier.issn | 0033-8362 | - |
| dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/209792 | - |
| dc.description.abstract | Purpose: 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.language | English | - |
| dc.publisher | Springer | - |
| dc.relation.isPartOf | RADIOLOGIA MEDICA | - |
| dc.relation.isPartOf | RADIOLOGIA MEDICA | - |
| dc.subject.MESH | Comprehension* | - |
| dc.subject.MESH | Female | - |
| dc.subject.MESH | Humans | - |
| dc.subject.MESH | Radiology* | - |
| dc.subject.MESH | Reproducibility of Results | - |
| dc.subject.MESH | Retrospective Studies | - |
| dc.title | Readability versus accuracy in LLM-transformed radiology reports: stakeholder preferences across reading grade levels | - |
| dc.type | Article | - |
| dc.contributor.googleauthor | Lee, Hong-Seon | - |
| dc.contributor.googleauthor | Kim, Sungjun | - |
| dc.contributor.googleauthor | Kim, Songsoo | - |
| dc.contributor.googleauthor | Seo, Jeongrok | - |
| dc.contributor.googleauthor | Kim, Won Hwa | - |
| dc.contributor.googleauthor | Kim, Jaeil | - |
| dc.contributor.googleauthor | Han, Kyunghwa | - |
| dc.contributor.googleauthor | Hwang, Shin Hye | - |
| dc.contributor.googleauthor | Lee, Young Han | - |
| dc.identifier.doi | 10.1007/s11547-025-02098-5 | - |
| dc.relation.journalcode | J02594 | - |
| dc.identifier.eissn | 1826-6983 | - |
| dc.identifier.pmid | 41023287 | - |
| dc.identifier.url | https://link.springer.com/article/10.1007/s11547-025-02098-5 | - |
| dc.subject.keyword | Large language models (LLMs) | - |
| dc.subject.keyword | Radiology reports | - |
| dc.subject.keyword | Readability and accuracy | - |
| dc.subject.keyword | Artificial intelligence in radiology | - |
| dc.subject.keyword | Patient-centered communication | - |
| dc.contributor.affiliatedAuthor | Lee, Hong-Seon | - |
| dc.contributor.affiliatedAuthor | Kim, Sungjun | - |
| dc.contributor.affiliatedAuthor | Kim, Songsoo | - |
| dc.contributor.affiliatedAuthor | Han, Kyunghwa | - |
| dc.contributor.affiliatedAuthor | Hwang, Shin Hye | - |
| dc.contributor.affiliatedAuthor | Lee, Young Han | - |
| dc.identifier.scopusid | 2-s2.0-105018204615 | - |
| dc.identifier.wosid | 001584241100001 | - |
| dc.citation.volume | 130 | - |
| dc.citation.number | 12 | - |
| dc.citation.startPage | 1986 | - |
| dc.citation.endPage | 1999 | - |
| dc.identifier.bibliographicCitation | RADIOLOGIA MEDICA, Vol.130(12) : 1986-1999, 2025-12 | - |
| dc.identifier.rimsid | 90708 | - |
| dc.type.rims | ART | - |
| dc.description.journalClass | 1 | - |
| dc.description.journalClass | 1 | - |
| dc.subject.keywordAuthor | Large language models (LLMs) | - |
| dc.subject.keywordAuthor | Radiology reports | - |
| dc.subject.keywordAuthor | Readability and accuracy | - |
| dc.subject.keywordAuthor | Artificial intelligence in radiology | - |
| dc.subject.keywordAuthor | Patient-centered communication | - |
| dc.subject.keywordPlus | ENGAGEMENT | - |
| dc.subject.keywordPlus | CARE | - |
| dc.type.docType | Article; Early Access | - |
| dc.description.isOpenAccess | N | - |
| dc.description.journalRegisteredClass | scie | - |
| dc.description.journalRegisteredClass | scopus | - |
| dc.relation.journalWebOfScienceCategory | Radiology, Nuclear Medicine & Medical Imaging | - |
| dc.relation.journalResearchArea | Radiology, Nuclear Medicine & Medical Imaging | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.