Harnessing generative pre-trained transformer technology for clinical decision support in retinal detachment

Basit Öğe Kaydı
dc.authorid0000-0001-7173-8617
dc.authorid0000-0002-8520-0073
dc.authorid0000-0003-1694-6864
dc.contributor.authorAğın, Abdullah
dc.contributor.authorÖztürk, Yücel
dc.contributor.authorKıvrak, Ulviye
dc.date.accessioned2025-08-27T13:48:59Z
dc.date.available2025-08-27T13:48:59Z
dc.date.issued2025
dc.departmentFakülteler, Tıp Fakültesi, Cerrahi Tıp Bilimleri Bölümü, Göz Hastalıkları Ana Bilim Dalı
dc.description.abstractAim: Considering the increasing incorporation of artificial intelligence (AI) in healthcare, it is crucial to comprehend the advantages and constraints of these technologies within ophthalmologic settings for their secure and efficient clinical utilization. This study aims to comprehensively assess the efficacy of three leading Generative Pre-trained Transformer (GPT) -based platforms in providing clinical decision-support for retinal detachment (RD). Methods: This cross-sectional comparative study was conducted between April 2024 and May 2024. Fifty questions were created based on the American Academy of Ophthalmology “Retina Book”, specifically targeting RD. The answers were produced by three different platforms and assessed by three independent reviewers who used Likert scales to evaluate their comprehensiveness and accuracy. Six readability metrics, including the Flesch-Kincaid Grade Level (FKGL) and Flesch Reading Ease Score (FRES), average words per sentence, average syllables per word, total sentence count, and total word count, were assessed. Results: Gemini earned the most outstanding results for comprehensiveness (4.11±0.72) and accuracy (1.49±0.61), followed by ChatGPT and Copilot. ChatGPT had superior readability metrics, achieving an FKGL of 15.62±2.85 and a FRES of 62.54±12.34, establishing it as the most accessible platform. ChatGPT demonstrated significantly higher performance compared to other platforms in the metrics of average syllables per word (p=0.0421) and total word count (p=0.0115). At the same time, no significant differences were found among the platforms in the metrics of average words per sentence (p=0.0842) and total sentence count (p=0.1603). Intraclass correlation coefficient (ICC) values indicated strong inter-rater agreement for comprehensiveness (ICC >0.74) and moderate to-high agreement for accuracy (ICC >0.56). Conclusion: Gemini’s detailed and accurate responses position it as a robust tool for professional use, while ChatGPT’s superior readability makes it suitable for patient education. These findings emphasize the synergistic advantages of AI platforms in research and development management and show the necessity for hybrid systems that integrate accessibility with accuracy.
dc.identifier.citationAğın, A., Öztürk, Y., & Kıvrak, U. (2025). Harnessing generative pre-trained transformer technology for clinical decision support in retinal detachment. The Medical Bulletin of Haseki, https://doi.org/10.4274/haseki.galenos.2025.79553
dc.identifier.doi10.4274/haseki.galenos.2025.79553
dc.identifier.issn2147-2688
dc.identifier.scopusqualityQ3
dc.identifier.urihttps://doi.org/10.4274/haseki.galenos.2025.79553
dc.identifier.urihttps://hdl.handle.net/20.500.13055/1091
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynak.otherESCI - Emerging Sources Citation Index
dc.institutionauthorÖztürk, Yücel
dc.institutionauthorid0000-0002-8520-0073
dc.language.isoen
dc.publisherGalenos Publishing House
dc.relation.ispartofThe Medical Bulletin of Haseki
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectArtificial Intelligence
dc.subjectReadability
dc.subjectOphthalmology
dc.subjectRetina
dc.subjectRetinal Detachment
dc.titleHarnessing generative pre-trained transformer technology for clinical decision support in retinal detachment
dc.typeArticle
dspace.entity.typePublication

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
Tam Metin / Full Text
Boyut:
573.26 KB
Biçim:
Adobe Portable Document Format
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
Kapalı Erişim
İsim:
license.txt
Boyut:
1.17 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Cerrahi Tıp Bilimleri Bölümü Koleksiyonu