Data, Decisions, and Care: A Review on Ethical Challenges and Mitigation Strategies in AI-Driven Healthcare Systems

Authors:

Mrs. D. Manasa¹, Thodupunuri Trikshala Goud²

¹Assistant Professor, Department of Computer Science and Engineering

St. Martin’s Engineering College, Hyderabad, India manasadodle@gmail.com

²Student, Department of Computer Science and Engineering

 St. Martin’s Engineering College, Hyderabad, India trikshalagoud@gmail.com

Abstract

The rapid integration of Artificial Intelligence in healthcare has ushered in a new era of clinical efficiency and diagnostic innovation, while simultaneously giving rise to a pressing set of ethical concerns. Issues of bias and fairness, lack of privacy and data security, complex and uninterpretable outcomes, and the absence of clear accountability and governance frameworks have emerged as recurring challenges that threaten equitable and safe patient care. This review examines these ethical dimensions in depth, drawing from a range of prior research to catalogue the challenges and summarize the mitigation strategies proposed in response. Key technical solutions identified include federated learning, differential privacy, adversarial training and xAI, while institutional approaches encompass ERM frameworks, national governance initiatives and multi-stakeholder collaboration. The review concludes that despite the growing body of proposed solutions, real-world deployment and outcome measurement remain significantly lacking, and that responsible AI integration in healthcare ultimately necessitates a shared effort across developers, clinicians, institutions and policymakers.

Keywords: Artificial Intelligence, Healthcare Ethics, Algorithmic Bias, Data Privacy, Differential Privacy, Explainable AI, Federated Learning, Enterprise Risk Management, Transparency, Accountability, Mitigation Strategies