Paper Contents
Abstract
The swift integration of machine learning (ML) into healthcare diagnostics is set to transform the sector by improving accuracy, efficiency, and accessibility. Nevertheless, the implementation of these algorithms brings forth considerable ethical dilemmas, especially concerning bias, fairness, privacy, and accountability. This paper examines the ethical ramifications of ML in healthcare diagnostics, emphasizing how data biases can sustain disparities in care, the difficulties in safeguarding patient privacy, and the necessity for transparency and accountability in algorithmic decision-making. Additionally, it reviews ongoing initiatives to tackle these challenges and suggests frameworks for the ethical development and application of machine learning algorithms within the healthcare domain.
Copyright
Copyright © 2025 Amit A. Patel. This is an open access article distributed under the Creative Commons Attribution License.
