Artificial intelligence (AI) is reshaping radiology by enhancing diagnostic accuracy, optimizing workflows, and supporting clinical decision-making. Despite over 500 FDA-approved algorithms, adoption remains limited due to ethical, legal, and operational challenges. Key concerns include data privacy, algorithmic bias, explainability, and accountability. Inadequate representation in training datasets can perpetuate healthcare disparities, while “black-box” decision-making undermines trust and complicates liability. Ethical governance must integrate transparency, fairness, and human oversight from system design through implementation. Data protection frameworks, such as GDPR and Türkiye’s KVKK, mandate anonymization, informed consent, and secure handling of imaging data. Privacy safeguards—metadata cleaning, pixel-level masking, and defacing—are essential to prevent re-identification. Commercial use of health data requires explicit consent, strict oversight, and equitable benefit-sharing. Explainable AI techniques and human-in-the-loop designs can improve trust and reliability. In Türkiye, AI-specific regulations are emerging, with the 2024 Draft AI Law introducing risk-based classif
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