Artificial Intelligence Ethics In Healthcare Management: A Review Of Patient Privacy, Bias, And Transparency

Abstract

The rapid integration of artificial intelligence (AI) into healthcare management has transformed efficiency, resource use, and patient care, but also introduced ethical challenges regarding privacy, bias, and transparency. AI’s reliance on sensitive data for predictive analytics and decision support increases risks of breaches and unauthorized sharing, highlighting the need for privacy-preserving methods like federated learning and differential privacy. Algorithmic bias, often rooted in non-representative datasets or flawed designs, can perpetuate healthcare inequities, underscoring inclusive data practices and auditing. The opacity of deep learning models undermines trust and accountability, reinforcing the importance of explainable AI and standardized reporting. Current gaps include the absence of consistent ethical metrics and scalable solutions. Future directions involve global governance frameworks and stakeholder engagement. Overall, a holistic strategy integrating technical, organizational, and regulatory approaches is essential for equitable and trustworthy AI in healthcare.

Keywords

• Artificial Intellegente
• Healthcare Management
• Patient Privacy

Sağlık Yönetiminde Yapay Zekânın Etik Boyutları: Hasta Gizliliği, Algoritmik Önyargı ve Şeffaflık Üzerine Bir Derleme

Abstract

Yapay zekânın (YZ) sağlık yönetiminde hızla yaygınlaşması, operasyonel verimlilik, kaynak tahsisi ve hasta bakımını dönüştürmüştür. Ancak bu entegrasyon; hasta gizliliği, algoritmik önyargı ve şeffaflık gibi önemli etik zorlukları gündeme getirmektedir. YZ’nin tahmine dayalı analitikler ve karar destek sistemlerinde hassas verilere bağımlılığı, veri ihlali risklerini artırmakta; federatif öğrenme ve diferansiyel gizlilik gibi yöntemlerin gerekliliğini göstermektedir. Algoritmik önyargı, temsili olmayan verilerden kaynaklanarak eşitsizlikleri sürdürebilir; bu nedenle kapsayıcı veri ve algoritmik denetim kritik önemdedir. Derin öğrenme gibi opak modeller ise güveni zedelemekte, açıklanabilir YZ ve standart raporlama protokolleri ihtiyacını ortaya koymaktadır. Literatürden elde edilen bulgular, etik ölçütlerin eksikliği, küresel yönetişim çerçeveleri ve paydaş katılımının gerekliliğini vurgulayarak adil, şeffaf ve güvenilir YZ için bütüncül stratejilere işaret etmektedir.

Keywords

• Yapay Zeka
• Sağlık Yönetimi
• Hasta Gizliliği

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