The integration of Artificial Intelligence (AI) into healthcare promises significant advancements, potentially revolutionizing diagnostics, treatment, and patient care. However, this technological surge brings forth a complex web of ethical considerations that demand careful scrutiny. While AI algorithms can analyze vast datasets to identify patterns invisible to the human eye, leading to earlier and more accurate diagnoses, the reliance on these systems raises concerns about data privacy and security. Patient data, often highly sensitive, becomes vulnerable to breaches and misuse. Furthermore, the 'black box' nature of some AI algorithms makes it difficult to understand how decisions are reached, potentially eroding trust and accountability. If algorithms are trained on biased datasets, they may perpetuate and even amplify existing health disparities, unfairly disadvantaging certain populations. The debate also revolves around the potential deskilling of healthcare professionals. If diagnosis and treatment planning are increasingly outsourced to AI, it could diminish the expertise and critical thinking skills of doctors and nurses. Therefore, robust ethical frameworks and regulatory oversight are crucial to ensure that AI in healthcare is deployed responsibly and equitably, prioritizing patient well-being above all else.