Ethical concerns related to AI in healthcare include algorithmic bias, patient consent, and transparency in decision-making. Bias in AI models can lead to disparities in healthcare outcomes, disproportionately affecting marginalized communities (Weidener & Fischer, 2024). AI algorithms are trained on historical data, which may contain inherent biases. If AI systems are trained predominantly on data from certain demographics, they may underperform when diagnosing or treating individuals from underrepresented groups. For example, AI-driven dermatology applications trained on lighter skin tones may struggle to detect conditions in darker-skinned patients, leading to diagnostic disparities. Another ethical issue is patient consent. In traditional medicine, patients are informed of the risks and benefits of a particular procedure or treatment before agreeing to it. However, AI-driven decisions are often based on complex machine-learning models that even healthcare professionals may not fully understand. This raises the question of how much information should be disclosed to patients when AI is involved in their diagnosis or treatment. Should a patient have the right to reject AI-based recommendations and opt for traditional medical evaluations instead? Transparency in AI decision-making is another critical ethical issue. Many AI systems operate as "black boxes," meaning their decision-making processes are not easily interpretable. This lack of explainability creates challenges for physicians who must justify AI-driven recommendations to their patients. Physicians are legally and ethically responsible for their treatment decisions, yet if an AI model suggests an unexpected diagnosis or treatment, it may be difficult to determine whether the AI or the human professional should be held accountable. To address these concerns, AI developers and healthcare organizations must prioritize fairness, transparency, and accountability. Strategies such as explainable AI (XAI), which makes AI decision-making more interpretable, can help build trust in AI-powered healthcare solutions.