A Literature Review on Example-Based Explanations in Medical Image Analysis

Area of Science:

• Artificial Intelligence in Medicine
• Medical Imaging Analysis
• Explainable AI (XAI)

Background:

• Deep learning models achieve high performance in medical imaging but lack transparency, leading to clinical distrust.
• Explainable AI (XAI) methods are crucial for understanding and validating AI predictions in healthcare.
• Example-based explanations are intuitive for medical practitioners but lack comprehensive review.

Purpose of the Study:

• To provide a comprehensive review of example-based explainability techniques in medical imaging.
• To analyze the strengths and limitations of existing example-based explanation methods.
• To identify barriers and future directions for deploying example-based XAI in clinical practice.

Main Methods:

• Systematic literature review of example-based explainability works in medical imaging.
• Analysis of methodologies, strengths, and limitations of identified studies.
• Identification of common challenges and future research avenues.

Main Results:

• Example-based explanations offer intuitive insights into AI model reasoning for medical tasks.
• Key limitations identified include the absence of objective evaluation metrics and clinical validation.
• Privacy concerns also pose a significant challenge to the practical implementation of these methods.

Conclusions:

• Example-based explanations show promise for improving trust in medical AI.
• Addressing the identified limitations is critical for successful clinical integration.
• Future research should focus on developing robust evaluation metrics and ensuring patient privacy.