Ethical Challenges of Black-Box AI in Healthcare Diagnostics

Abstract

Artificial Intelligence has rapidly transformed healthcare diagnostics by enabling automated disease detection, medical image analysis, and clinical decision support. Deep learning models, particularly neural networks, have demonstrated remarkable accuracy in diagnosing conditions such as cancer, cardiovascular diseases, and neurological disorders. However, many of these systems operate as black-box models, offering little or no insight into how diagnostic decisions are made. This opacity raises significant ethical concerns related to accountability, transparency, patient trust, clinical responsibility, and safety. In healthcare, where decisions can directly impact human life, the inability to explain AI-driven diagnoses challenges established ethical and medical principles. This paper examines the ethical challenges posed by black-box AI in healthcare diagnostics. It analyzes issues of trust, bias, accountability, informed consent, and regulatory compliance, while highlighting the limitations of opaque models. The paper further discusses the role of explainable AI as a pathway toward ethically responsible diagnostic systems and argues for the integration of transparency as a core requirement in medical AI deployment.

Keywords: Black-Box AI, Healthcare Diagnostics, Ethical AI, Explainable AI, Medical Decision Support