White Coat Oversight of Black-Box Algorithms: Ethical Challenges in the Application of Artificial Intelligence in Healthcare

Abstract

Artificial intelligence (AI) is rapidly influencing the future of healthcare by increasing diagnostic accuracy, supporting personalised treatments, and improving system efficiency. This paper examines the ethical and regulatory issues that arise from incorporating AI into medical practice. Drawing on the evolution of AI from early systems such as MYCIN to more recent applications such as convolutional neural networks in imaging, the discussion highlights the importance of ethical oversight from the outset of development. Central themes include the necessity for transparency, strong data protection measures, algorithmic fairness, and responsible deployment. Explainable AI (XAI) technologies, international regulatory responses such as the European Union's AI Act, and inclusive design strategies are explored as key tools for ensuring equity in care delivery. Risks, including data misuse, embedded bias in training sets, and inappropriate reliance on opaque systems, are analysed with real-world examples. Ultimately, the paper calls for interdisciplinary cooperation among healthcare providers, developers, and regulators to create systems that enhance patient outcomes while remaining aligned with ethical and societal values.

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