Ethical Considerations in Using Machine Learning for Healthcare Applications

EasyChair Preprint 13565

Abstract

Machine learning (ML) has the potential to revolutionize healthcare by enabling advanced diagnostics, personalized treatments, and improved patient outcomes. However, the ethical implications of using ML in healthcare cannot be overlooked. This abstract explores the key ethical considerations that need to be addressed when applying ML in healthcare applications.

Privacy and data protection are crucial concerns in healthcare ML. Safeguarding patient data, obtaining informed consent, and implementing secure storage and transmission mechanisms are essential to maintain confidentiality and trust. Bias and fairness issues must also be addressed, including identifying and mitigating biases in training data, ensuring fairness in algorithmic decision-making, and providing transparency in the process.

Keyphrases: Accessibility, Equity, Governance, Regulatory Compliance, ethical review, fairness

@booklet{EasyChair:13565,
  author    = {Godwin Olaoye},
  title     = {Ethical Considerations in Using Machine Learning for Healthcare Applications},
  howpublished = {EasyChair Preprint 13565},
  year      = {EasyChair, 2024}}