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YAPAY ZEKA ETİĞİ İLE AKREDİTASYONUN UYUMU: SAĞLIK HİZMETLERİ AKREDİTASYON ÇERÇEVELERİNDE YAPAY ZEKA DENETİMİNİN DEĞERLENDİRİLMESİ

BRIDGING ARTIFICIAL INTELLIGENCE ETHICS AND ACCREDITATION: ASSESSING ARTIFICIAL INTELLIGENCE OVERSIGHT IN HEALTHCARE ACCREDITATION FRAMEWORKS

Zuhal Çayırtepe(Türkiye Sağlık Enstitüleri Başkanlığı)0000-0002-9507-9916
Bayram Demir(Türkiye Sağlık Enstitüleri Başkanlığı)0000-0003-2547-2664
Sayfa
687–706
Yayın Tarihi
2026-09-23
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Yapay Zeka Hızlı Bakış & Çıkarımlar

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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Sonuç olarak mevcut akreditasyon çerçeveleri YZ yönetişimi için sağlam bir temel sunsa da, YZ’ye özgü somut denetim kriterleri aracılığıyla daha fazla geliştirilmelidir.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Yapay zeka (YZ), tanı süreçlerini, klinik karar verme mekanizmalarını ve sistem verimliliğini iyileştirerek sağlık hizmeti sunumunu hızla dönüştürmektedir.
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Temel Bulgular & Veri:Bu çalışma, iki akreditasyon çerçevesinin sağlıkta güvenli YZ kullanımını denetleme konusundaki etik gözetim kapasitesini değerlendirmeyi ve geliştirilmesi gereken alanları belirlemeyi amaçlamaktadır.
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Sonuç & Katkı:Sonuç olarak mevcut akreditasyon çerçeveleri YZ yönetişimi için sağlam bir temel sunsa da, YZ’ye özgü somut denetim kriterleri aracılığıyla daha fazla geliştirilmelidir.
🔬Tür:Uygulamalı Anket & Alan Araştırması
📍Kapsam:2024 Verileri
⚙️Değişkenler:yapay zeka · etik yönetişim · sağlık hizmetleri akreditasyonu

Özet

Yapay zeka (YZ), tanı süreçlerini, klinik karar verme mekanizmalarını ve sistem verimliliğini iyileştirerek sağlık hizmeti sunumunu hızla dönüştürmektedir. Ancak bu entegrasyon; güvenlik, şeffaflık, hesap verebilirlik ve hakkaniyet gibi kritik etik zorlukları da beraberinde getirmektedir. DSÖ etik çerçevesi ve AB YZ Yasası gibi küresel düzenleyici girişimler üst düzey rehberlik sağlasa da bu ilkelerin kalite ve akreditasyon standartları (KAS) bünyesinde nasıl somutlaştırıldığına dair sınırlı ampirik kanıt bulunmaktadır. Bu çalışma, iki akreditasyon çerçevesinin sağlıkta güvenli YZ kullanımını denetleme konusundaki etik gözetim kapasitesini değerlendirmeyi ve geliştirilmesi gereken alanları belirlemeyi amaçlamaktadır. Çalışmada, sağlıkta YZ için DSÖ’nün altı etik ilkesini temel alan yönlendirilmiş nitel içerik analizi uygulanmıştır. Bu kapsamda TÜSKA SAS ve JCI-AS (8. Baskı, 2024) akreditasyon sistemleri amaçlı örnekleme yöntemiyle seçilmiştir. 25 alt temadan oluşan bir kodlama şeması geliştirilerek standartlara ve ölçülebilir unsurlara (ÖU) uygulanmıştır. Her bir ÖU, YZ’ye özgü etik gözetim açısından "denetlenebilir", "kısmen denetlenebilir" veya "denetlenemez" olarak sınıflandırılmıştır. Kodlama süreci iki araştırmacı tarafından bağımsız olarak yürütülmüş; kodlayıcılar arası güvenirlik Cohen’s kappa katsayısı ($\kappa = 0.88$) ile güçlü bir uyum sergilemiştir. TÜSKA SAS’tan 73, JCI-AS’tan ise 121 ÖU en az bir etik alt tema ile ilişkilendirilmiştir. JCI-AS, TÜSKA SAS’a (7/25) kıyasla daha yüksek denetlenebilirlik (11/25 alt tema) göstermiştir. Her iki sistemin de insan gözetimi, bilgilendirilmiş onam, gizlilik ve hesap verebilirlik konularında güçlü olduğu görülmüştür. Ancak; algoritmik şeffaflık, yanlılığın azaltılması, hakkaniyet yönetişimi, çevresel sürdürülebilirlik ve kriz hazırlığı gibi YZ’ye özgü alanlarda önemli boşluklar saptanmıştır. Sonuç olarak mevcut akreditasyon çerçeveleri YZ yönetişimi için sağlam bir temel sunsa da, YZ’ye özgü somut denetim kriterleri aracılığıyla daha fazla geliştirilmelidir. Bu sistemlerin şeffaflık, adalet ve hesap verebilirlik için ölçülebilir standartlarla güçlendirilmesi elzemdir. Akreditasyon sistemlerinin sürekli iyileştirme yapısından yararlanmak; bu mekanizmaları proaktif birer yönetişim aracına dönüştürmek ve sağlık sistemlerinde güvenli, hakkaniyetli ve güvenilir bir YZ entegrasyonu sağlamak için stratejik bir fırsat sunmaktadır.

Abstract

Artificial intelligence (AI) is rapidly transforming healthcare delivery by improving diagnostics, clinical decision-making, and system efficiency. However, its integration introduces critical ethical challenges, including safety, transparency, accountability, and equity. Although global regulatory initiatives such as the WHO ethical framework and the EU AI Act provide high-level guidance, there is limited empirical evidence on how these principles are operationalized within quality and accreditation standards (QAS). This study aims to evaluate the ethical oversight capacity of two accreditation frameworks in auditing the safe use of AI in healthcare and to identify areas requiring further development. A directed qualitative content analysis was conducted using the WHO’s six ethical principles for AI in health as the analytical framework. Two major accreditation systems—TÜSKA SAS and JCI-AS (8th Edition, 2024) —were purposively selected. A coding scheme consisting of 25 subthemes was developed and applied to standards and measurable elements (MEs). Each ME was classified as auditable, partially auditable, or not auditable in relation to AI-specific ethical oversight. Coding was performed independently by two researchers, and inter-coder reliability was assessed using Cohen’s kappa coefficient, demonstrating strong agreement (κ = 0.88). Discrepancies were resolved through consensus and expert consultation. A total of 73 MEs from TÜSKA SAS and 121 MEs from JCI-AS were mapped to at least one ethical subtheme. JCI-AS showed higher auditability (11/25 subthemes) compared to TÜSKA SAS (7/25). Both frameworks demonstrated strengths in human oversight, informed consent, privacy, and accountability. However, significant gaps were identified in AI-specific domains, including algorithmic transparency, bias mitigation, equity governance, environmental sustainability, and crisis preparedness. Some subthemes, such as public health optimization and crisis mitigation, were entirely absent. Ultimately the selected accreditation frameworks provide a solid foundation for AI governance but require further development through explicit, AI-specific audit criteria. Strengthening these systems with measurable standards for transparency, fairness, and accountability is essential. Leveraging their continuous improvement structure offers a strategic opportunity to transform accreditation into a proactive governance instrument, ensuring the safe, equitable, and trustworthy integration of AI into healthcare systems.

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Bu Makaleye Atıf Yap

Çayırtepe, Z., Demir, B. (2026). YAPAY ZEKA ETİĞİ İLE AKREDİTASYONUN UYUMU: SAĞLIK HİZMETLERİ AKREDİTASYON ÇERÇEVELERİNDE YAPAY ZEKA DENETİMİNİN DEĞERLENDİRİLMESİ. Hacettepe Sağlık İdaresi Dergisi, Cilt: 29 Sayı: 3, 687–706. https://doi.org/10.61859/hacettepesid.1922869

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