The rapid integration of artificial intelligence in healthcare organizations presents significant opportunities for improving diagnosis, treatment planning, operational efficiency, and patient outcomes, but it simultaneously raises complex ethical and practical challenges that must be carefully managed. One of the foremost ethical concerns is related to patient privacy and data security. AI systems rely heavily on large volumes of health data, including electronic health records, imaging data, and genomic information. The collection, storage, and processing of such sensitive data increase the risk of breaches, unauthorized access, and misuse. Inadequate safeguards can compromise patient confidentiality and erode trust in healthcare systems. Moreover, questions arise about informed consent, as patients may not fully understand how their data is being used in AI algorithms or shared across institutions. Another critical issue is algorithmic bias and fairness. AI systems are only as good as the data they are trained on, and if the training datasets are unrepresentative or biased, the outcomes may perpetuate or even exacerbate existing health disparities. For instance, an AI model trained predominantly on data from a specific demographic group may perform poorly when applied to diverse populations, leading to misdiagnosis or unequal treatment. This raises concerns about equity and justice in healthcare delivery, as marginalized populations may be disproportionately affected by flawed AI systems. Transparency and explainability also pose ethical challenges. Many AI models, particularly deep learning systems, function as “black boxes,” making it difficult for healthcare professionals to understand how decisions are made. This lack of interpretability can hinder clinical decision-making and accountability. Physicians may be reluctant to rely on AI recommendations without clear explanations, and patients may question the validity of decisions that cannot be adequately justified. Furthermore, in cases of medical errors involving AI, determining liability becomes complicated. It is unclear whether responsibility lies with the healthcare provider, the software developer, or the institution implementing the technology. From a practical perspective, the