Ethical Challenges of AI in Medicine: Balancing Innovation, Privacy, and Equit
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Artificial Intelligence, Health Equity, Medical Ethics
Abstract
Rapid advancements in artificial intelligence (AI) have transformed medical practice, offering unprecedented capabilities in diagnosis, treatment planning, and predictive analytics. These innovations, however, introduce complex ethical challenges related to patient privacy, algorithmic transparency, equity, and accountability. Growing reliance on AI in clinical environments has heightened concerns about data governance, bias in machine learning models, and uneven access to AI-enabled healthcare tools. This study aims to analyze the ethical tensions arising from AI integration in medicine and to identify strategies that balance technological innovation with the protection of fundamental ethical principles. A qualitative meta-synthesis approach was employed, drawing on peer-reviewed literature, policy documents, and real-world case analyses to examine patterns of ethical risk and mitigation frameworks. The findings reveal that privacy vulnerabilities, inequitable algorithmic performance, and opacity in decision-making processes represent the most frequent ethical concerns. The results also show that robust governance structures, transparent AI design, and inclusive dataset practices significantly reduce ethical risks. The study concludes that responsible AI in medicine requires a multidimensional ethical framework that integrates patient rights, algorithmic fairness, and institutional accountability. Strengthening interdisciplinary collaboration and regulatory oversight is essential for ensuring that AI-driven innovation benefits all patients equitably.
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