Artificial intelligence (AI) is rapidly transforming diagnostic radiology by enhancing image interpretation, workflow efficiency, and clinical decision-making. Advanced AI algorithms, particularly those based on machine learning and deep learning, have demonstrated promising performance in tasks such as lesion detection, disease classification, and prognostic assessment across multiple imaging modalities. Despite these advancements, the integration of AI into routine radiological practice presents significant ethical and clinical challenges that must be carefully addressed to ensure safe, effective, and equitable use. This mini review explores the key ethical concerns associated with AI implementation in diagnostic radiology, including data privacy, algorithmic bias, transparency, and accountability in clinical decision-making. The reliance on large datasets for AI training raises concerns regarding patient consent, data security, and representativeness, which may influence algorithm performance across diverse populations. From a clinical perspective, challenges such as workflow integration, validation across different imaging systems, regulatory approval, and the need for continuous performance monitoring are critically examined. The impact of AI on professional roles and responsibilities of radiologists is also discussed, emphasizing the importance of human oversight and collaborative decision-making. The review highlights that while AI has the potential to augment radiological practice and improve patient outcomes, its successful implementation depends on robust ethical frameworks, standardized validation protocols, and interdisciplinary collaboration among clinicians, engineers, policymakers, and patients. Addressing these challenges proactively will support the responsible adoption of AI and ensure that technological innovation aligns with the core principles of patient-centered care in radiology.

Keywords: Artificial intelligence; Diagnostic radiology; Medical imaging; Ethics;