Ethical Considerations in Deploying Machine Learning Models in Healthcare

Abstract

The integration of machine learning models into healthcare systems offers unprecedented opportunities for improving diagnostic accuracy, treatment planning, and patient outcomes. However, this technological advancement brings forth a host of ethical considerations that demand careful scrutiny. This research paper explores the multifaceted ethical landscape surrounding the deployment of machine learning models in healthcare settings. The ethical considerations encompass a spectrum of issues, including but not limited to patient privacy, transparency in algorithmic decision-making, bias mitigation, and the potential impact on healthcare disparities. The paper examines existing ethical frameworks and proposes guidelines for ensuring responsible and patient-centric deployment of machine learning in the medical domain.It explores cases where ethical considerations may conflict with the pursuit of optimal algorithmic performance, emphasizing the importance of prioritizing patient well-being and maintaining public trust.