LuminaConsent: AI-driven standardization and quality enhancement of urological informed consent documentation

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dc.authorid0000-0001-7685-8597
dc.authorid0000-0001-7595-8879
dc.authorid0000-0001-7577-7955
dc.authorid0009-0003-2194-1563
dc.authorid0000-0002-9864-3156
dc.authorid0000-0002-0490-8353
dc.authorid0000-0002-7461-1942
dc.authorid0009-0000-2098-827X
dc.authorid0000-0002-3344-464X
dc.authorid0000-0001-5700-0835
dc.contributor.authorTopçu, İbrahim
dc.contributor.authorSoylu, Tuncay
dc.contributor.authorŞimşekoğlu, Muhammed Fatih
dc.contributor.authorTuzcu, Esra Melis
dc.contributor.authorSalman, Zeynep
dc.contributor.authorDemir, Perihan
dc.contributor.authorKaç, Beyzanur
dc.contributor.authorKartal, Muhammed Yusuf
dc.contributor.authorSuzan, Serhat
dc.contributor.authorKaraman, Muhammet İhsan
dc.date.accessioned2026-05-11T09:29:39Z
dc.date.available2026-05-11T09:29:39Z
dc.date.issued2026
dc.departmentFakülteler, Tıp Fakültesi, Temel Tıp Bilimleri Bölümü, Tıp Tarihi ve Etik Ana Bilim Dalı
dc.description.abstractObjective: Informed consent is the cornerstone of modern medical ethics, but current documentation systems negatively impact patient autonomy and clinical quality due to deficiencies in readability, comprehensibility, and standardization. These is sues hinder patient participation and require innovative solutions. This study introduces the AI-powered LuminaConsent system to address standard deficiencies, comprehensibility issues, and efficiency constraints in urological informed consent documents. Methods: In a three-armed comparative study, LuminaConsent (artificial intelligence), Turkish Urological Surgery Asso ciation standard forms, and expert-developed documents were evaluated in 10 urological procedures. The system is based on the RAG architecture, which uses OpenAI’s GPT-4o-mini model and a special knowledge base consisting of 12 clinical publications. Three independent urology specialists conducted a blind evaluation using a 100-point scale across five areas: scientific content accuracy, patient communication effectiveness, quality of risk-benefit information, perioperative guidance, and legal-ethical compliance. RESULTS: LuminaConsent achieved higher performance with mean scores of 82.33 points (SD±4.2) versus 78.77 points (SD±6.1) for professional society standards and 57.43 points (SD±3.8) for specialist documentation, representing statisti cally significant improvements of 43.3% over specialist practices (p<0.001) and 4.5% over professional society standards (p<0.05). The system demonstrated consistent high-quality output across all procedures while generating comprehensive documentation within 96-180 seconds compared to traditional processes requiring multiple days. Conclusion: LuminaConsent offers a pioneering model for systematic AI integration in clinical practice with its evidence based content generation and bilingual processing capabilities. The findings support the potential to empower patient auton omy, reduce application variations, and improve ethical standards.
dc.identifier.citationTopçu, İ., Soylu, T., Şimşekoğlu, M. F., Tuzcu, E. M., Salman, Z., Demir, P., Kaç, B., Kartal, M. Y., Suzan, S., & Karaman, M. İ. (2026). LuminaConsent: AI-driven standardization and quality enhancement of urological informed consent documentation. Northern Clinics of Istanbul, 13(2), pp. 1-13. https://doi.org/10.14744/nci.2026.93296
dc.identifier.doi10.14744/nci.2026.93296
dc.identifier.endpage13
dc.identifier.issn2148-4902
dc.identifier.issn2536-4553
dc.identifier.issue2
dc.identifier.scopusqualityQ3
dc.identifier.startpage1
dc.identifier.urihttps://doi.org/10.14744/nci.2026.93296
dc.identifier.urihttps://hdl.handle.net/20.500.13055/1474
dc.identifier.volume13
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.indekslendigikaynakTR-Dizin
dc.indekslendigikaynak.otherESCI - Emerging Sources Citation Index
dc.institutionauthorKaraman, Muhammet İhsan
dc.institutionauthorid0000-0001-5700-0835
dc.language.isoen
dc.publisherKare Publishing
dc.relation.ispartofNorthern Clinics of Istanbul
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectArtificial Intelligence
dc.subjectClinical Decision Support
dc.subjectInformed Consent
dc.subjectMedical Documentation
dc.subjectMedical Ethics
dc.subjectPatient Safety
dc.titleLuminaConsent: AI-driven standardization and quality enhancement of urological informed consent documentation
dc.typeArticle
dspace.entity.typePublication

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