ETHICAL CONSIDERATIONS IN AI-DRIVEN HEALTHCARE INNOVATIONS

Abstract

Artificial intelligence (AI) is reshaping healthcare across diagnostics, therapeutics, operations, and population health. Yet its benefits are coupled with ethical risks—privacy violations, bias and inequity, opacity, safety failures, accountability gaps, and governance deficits. This article synthesizes leading guidance and regulatory trajectories to propose a lifecycle-based ethical framework for AI in health. We map risks to stages from data acquisition to post-market monitoring; discuss fairness, transparency, and safety by design; and translate principles into implementable safeguards such as data minimization, participatory design, model documentation, human-in-the-loop controls, and continuous performance surveillance. We conclude with an implementation checklist tailored to low- and middle-income country (LMIC) settings and a research agenda for trustworthy, equitable clinical AI.