Fairness and Ethics in Artificial Intelligence-Based Medical Imaging

View Sample PDF

Author(s): Satvik Tripathi (College of Computing & Informatics, Drexel University, USA)and Thomas Heinrich Musiolik (Berlin University of the Arts, Germany)
Copyright: 2023
Pages: 12
Source title: Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-6684-7544-7.ch005

Keywords: Knowledge Management and Library Science / Medical Diagnosis and Practice / Medical Imaging / Medical Information Science Reference

Purchase

View Fairness and Ethics in Artificial Intelligence-Based Medical Imaging on the publisher's website for pricing and purchasing information.

Abstract

Artificial intelligence has a huge array of current and potential applications in healthcare and medicine. Ethical issues arising due to algorithmic biases are one of the greatest challenges faced in the generalizability of AI models today. The authors address safety and regulatory barriers that impede data sharing in medicine as well as potential changes to existing techniques and frameworks that might allow ethical data sharing for machine learning. With these developments in view, they also present different algorithmic models that are being used to develop machine learning-based medical systems that will potentially evolve to be free of the sample, annotator, and temporal bias. These AI-based medical imaging models will then be completely implemented in healthcare facilities and institutions all around the world, even in the remotest areas, making diagnosis and patient care both cheaper and freely accessible.

The IRMA Community

Research IRM

Fairness and Ethics in Artificial Intelligence-Based Medical Imaging

Purchase

Abstract

Related Content

IRMA Sponsors