Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

AI-Driven Healthcare: A Review on Ensuring Fairness and Mitigating Bias

Sribala Vidyadhari Chinta, Zichong Wang, Avash Palikhe, Xingyu Zhang, Ayesha Kashif, Monique Antoinette Smith, Jun Liu, Wenbin Zhang

TL;DR

The paper surveys AI-driven healthcare with a focus on fairness and bias, categorizing biases across the ML pipeline and reviewing bias detection and mitigation strategies. It presents fairness metrics and trade-offs, and discusses ethical, legal, and policy considerations. The work contributes a practical framework linking bias sources to concrete detection and mitigation methods, illustrated by domain-specific examples. It highlights the need for diverse data, transparency, and interdisciplinary collaboration to realize equitable AI-enabled healthcare.

Abstract

Artificial intelligence (AI) is rapidly advancing in healthcare, enhancing the efficiency and effectiveness of services across various specialties, including cardiology, ophthalmology, dermatology, emergency medicine, etc. AI applications have significantly improved diagnostic accuracy, treatment personalization, and patient outcome predictions by leveraging technologies such as machine learning, neural networks, and natural language processing. However, these advancements also introduce substantial ethical and fairness challenges, particularly related to biases in data and algorithms. These biases can lead to disparities in healthcare delivery, affecting diagnostic accuracy and treatment outcomes across different demographic groups. This review paper examines the integration of AI in healthcare, highlighting critical challenges related to bias and exploring strategies for mitigation. We emphasize the necessity of diverse datasets, fairness-aware algorithms, and regulatory frameworks to ensure equitable healthcare delivery. The paper concludes with recommendations for future research, advocating for interdisciplinary approaches, transparency in AI decision-making, and the development of innovative and inclusive AI applications.

AI-Driven Healthcare: A Review on Ensuring Fairness and Mitigating Bias

TL;DR

The paper surveys AI-driven healthcare with a focus on fairness and bias, categorizing biases across the ML pipeline and reviewing bias detection and mitigation strategies. It presents fairness metrics and trade-offs, and discusses ethical, legal, and policy considerations. The work contributes a practical framework linking bias sources to concrete detection and mitigation methods, illustrated by domain-specific examples. It highlights the need for diverse data, transparency, and interdisciplinary collaboration to realize equitable AI-enabled healthcare.

Abstract

Artificial intelligence (AI) is rapidly advancing in healthcare, enhancing the efficiency and effectiveness of services across various specialties, including cardiology, ophthalmology, dermatology, emergency medicine, etc. AI applications have significantly improved diagnostic accuracy, treatment personalization, and patient outcome predictions by leveraging technologies such as machine learning, neural networks, and natural language processing. However, these advancements also introduce substantial ethical and fairness challenges, particularly related to biases in data and algorithms. These biases can lead to disparities in healthcare delivery, affecting diagnostic accuracy and treatment outcomes across different demographic groups. This review paper examines the integration of AI in healthcare, highlighting critical challenges related to bias and exploring strategies for mitigation. We emphasize the necessity of diverse datasets, fairness-aware algorithms, and regulatory frameworks to ensure equitable healthcare delivery. The paper concludes with recommendations for future research, advocating for interdisciplinary approaches, transparency in AI decision-making, and the development of innovative and inclusive AI applications.
Paper Structure (33 sections, 3 equations, 1 figure, 1 table)

This paper contains 33 sections, 3 equations, 1 figure, 1 table.

Table of Contents

  1. Applications of AI in Healthcare
  2. Cardiology
  3. Ophthalmology
  4. Dermatology
  5. Neurology
  6. Radiology and Cancer Treatments
  7. Emergency Medicine and Critical Care
  8. Fairness Concerns in Healthcare
  9. Sources of Bias
  10. Pre-Processing Stage
  11. In-Processing Stage
  12. Post-Processing Stage
  13. Potential consequences of biases
  14. Fairness Metrics and Trade-offs
  15. Addressing and Mitigating Unfairness in AI
  16. ...and 18 more sections

Figures (1)

  • Figure 1: Illustration of the trade-off between performance-driven result and group fairness-driven result in the context of organ allocation, where gender is the sensitive attribute.