A COMPARATIVE STUDY: PERFORMANCE OF LARGE LANGUAGE MODELS IN SIMPLIFYING TURKISH COMPUTED TOMOGRAPHY REPORTS

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dc.contributor.authorCamur, Eren
dc.contributor.authorCesur, Turay
dc.contributor.authorGunes, Yasin Celal
dc.date.accessioned2025-01-21T16:34:55Z
dc.date.available2025-01-21T16:34:55Z
dc.date.issued2024
dc.departmentKırıkkale Üniversitesi
dc.description.abstractObjective: This study evaluated the effectiveness of various large language models (LLMs) in simplifying Turkish Computed Tomograpghy (CT) reports, a common imaging modality. Material and Method: Using fictional CT findings, we followed the Standards for Reporting of Diagnostic Accuracy Studies (STARD) and the Declaration of Helsinki. Fifty fictional Turkish CT findings were generated. Four LLMs (ChatGPT 4, ChatGPT-3.5, Gemini 1.5 Pro, and Claude 3 Opus) simplified reports using the prompt: Please explain them in a way that someone without a medical background can understand in Turkish. Evaluations were based on the Ate man & sacute; Readability Index and Likert scale for accuracy and readability. Results: Claude 3 Opus scored the highest in readability (58.9), followed by ChatGPT-3.5 (54.5), Gemini 1.5 Pro (53.7), and ChatGPT 4 (45.1). Likert scores for Claude 3 Opus (mean: 4.7) and ChatGPT 4 (mean: 4.5) showed no significant difference (p>0.05). ChatGPT 4 had the highest word count (96.98) compared to Claude 3 Opus (90.6), Gemini 1.5 Pro (74.4), and ChatGPT-3.5 (38.7) (p<0.001). Conclusion: This study shows that LLMs can simplify Turkish CT reports at a level that individuals without medical knowledge can understand and with high readability and accuracy. ChatGPT 4 and Claude 3 Opus produced the most comprehensible simplifications. Claude 3 Opus' simpler sentences may make it the optimal choice for simplifying Turkish CT reports.
dc.identifier.doi10.26650/IUITFD.1494572
dc.identifier.endpage326
dc.identifier.issn1305-6441
dc.identifier.issue4
dc.identifier.startpage321
dc.identifier.urihttps://doi.org/10.26650/IUITFD.1494572
dc.identifier.urihttps://hdl.handle.net/20.500.12587/24034
dc.identifier.volume87
dc.identifier.wosWOS:001334951900001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherIstanbul Univ, Fac Medicine, Publ Off
dc.relation.ispartofJournal of Istanbul Faculty of Medicine-Istanbul Tip Fakultesi Dergisi
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241229
dc.subjectLarge language model; radiology reports; readability; computed tomography; Turkish; simplifying
dc.titleA COMPARATIVE STUDY: PERFORMANCE OF LARGE LANGUAGE MODELS IN SIMPLIFYING TURKISH COMPUTED TOMOGRAPHY REPORTS
dc.typeArticle

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