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Addressing fairness issues in deep learning-based medical image analysis: a systematic review.

Zikang Xu^1,2, Jun Li³, Qingsong Yao³

¹School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, PR China.

NPJ Digital Medicine

|October 17, 2024

Summary

Deep learning in medical image analysis (MedIA) shows performance gaps for subgroups like elderly females. This survey reviews methods to evaluate and mitigate unfairness, aiming for equitable AI in healthcare.

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Area of Science:

Artificial Intelligence
Medical Imaging
Health Equity

Background:

Deep learning algorithms show high efficacy in medical image analysis (MedIA).
Performance disparities exist, with algorithms underperforming in specific subgroups (e.g., elderly females).
Addressing fairness in MedIA is crucial for equitable healthcare.

Purpose of the Study:

To survey current advancements in addressing fairness issues in MedIA.
To categorize studies into fairness evaluation and unfairness mitigation.
To foster understanding and collaboration between AI scientists and clinicians.

Main Methods:

Introduction to group fairness concepts.
Categorization of fair MedIA studies.
Detailed presentation of methodological approaches for fairness evaluation and mitigation.

Main Results:

Identification of key methodological approaches for fairness in MedIA.
Overview of techniques for evaluating and mitigating algorithmic unfairness.
Synthesis of current research on fair MedIA.

Conclusions:

Fairness in MedIA is a critical area requiring collaborative efforts.
Further development of mitigation methods is needed for an equitable MedIA society.
Establishing fair MedIA and healthcare systems presents ongoing challenges and opportunities.