AI and Ethical Dilemmas in Predictive Healthcare Algorithms

Abstract

Artificial intelligence has rapidly transformed the healthcare sector through the development of predictive healthcare algorithms capable of analyzing large volumes of medical data and identifying patterns that support diagnosis, treatment planning, and disease prediction. These algorithms are increasingly used in hospitals, insurance systems, and clinical research environments to enhance decision-making and improve patient outcomes. Despite these advantages, the integration of artificial intelligence in predictive healthcare has raised significant ethical concerns regarding fairness, transparency, accountability, and patient privacy. Ethical dilemmas arise when algorithmic decisions influence medical treatment without adequate oversight, when biased training datasets lead to unequal healthcare outcomes, or when predictive systems operate as opaque “black boxes” that clinicians cannot easily interpret. This research examines the ethical challenges associated with predictive healthcare algorithms and explores how responsible AI frameworks can address these dilemmas. The study analyzes issues related to algorithmic bias, data governance, explainability, patient consent, and accountability within healthcare AI systems.