Comparative Analysis of Large Language Models in Simplifying Turkish Ultrasound Reports to Enhance Patient Understanding

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dc.authoridCamur, Eren/0000-0002-8774-5800
dc.authoridCesur, Turay/0000-0002-2726-8045
dc.contributor.authorGunes, Yasin Celal
dc.contributor.authorCesur, Turay
dc.contributor.authorCamur, Eren
dc.date.accessioned2025-01-21T16:36:41Z
dc.date.available2025-01-21T16:36:41Z
dc.date.issued2024
dc.departmentKırıkkale Üniversitesi
dc.description.abstractObjective: To evaluate and compare the abilities of Language Models (LLMs) in simplifying Turkish ultrasound (US) findings for patients. Methods: We assessed the simplification performance of four LLMs: ChatGPT 4, Gemini 1.5 Pro, Claude 3 Opus, and Perplexity, using fifty fictional Turkish US findings. Comparison was based on Ate man's Readability Index and word count. Three radiologists rated medical accuracy, consistency, and comprehensibility on a Likert scale from 1 to 5. Statistical tests (Friedman, Wilcoxon, and Spearman correlation) examined differences in LLMs' performance. Results: Gemini 1.5 Pro, ChatGPT-4, and Claude 3 Opus received high Likert scores for medical accuracy, consistency, and comprehensibility (mean: 4.7-4.8). Perplexity scored significantly lower (mean: 4.1, p<0.001). Gemini 1.5 Pro achieved the highest readability score (mean: 61.16), followed by ChatGPT-4 (mean: 58.94) and Claude 3 Opus (mean: 51.16). Perplexity had the lowest readability score (mean: 47.01). Gemini 1.5 Pro and ChatGPT-4 used significantly more words compared to Claude 3 Opus and Perplexity (p<0.001). Linear correlation analysis revealed a positive correlation between word count of fictional US findings and responses generated by Gemini 1.5 Pro (correlation coefficient = 0.38, p<0.05) and ChatGPT-4 (correlation coefficient = 0.43, p<0.001). Conclusion: This study highlights strong potential of LLMs in simplifying Turkish US and Claude 3 Opus performed well, highlighting their effectiveness in healthcare communication. Further research is required to fully understand the integration of making.
dc.description.sponsorshipFunding : No funding was received for this study.
dc.identifier.doi10.58600/eurjther2225
dc.identifier.issn2564-7784
dc.identifier.issn2564-7040
dc.identifier.urihttps://doi.org/10.58600/eurjther2225
dc.identifier.urihttps://hdl.handle.net/20.500.12587/24366
dc.identifier.wosWOS:001289504400001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherPera Yayincilik Hizmetleri
dc.relation.ispartofEuropean Journal of Therapeutics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241229
dc.subjectLarge Language Models; ChatGPT; Claude 3 Opus; Ultrasound; Simplify
dc.titleComparative Analysis of Large Language Models in Simplifying Turkish Ultrasound Reports to Enhance Patient Understanding
dc.typeArticle

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