A review of explainable AI techniques and their evaluation in mammography for breast cancer screening

Abstract

Explainable AI (XAI) methods are gaining prominence in medical imaging, addressing the critical need for transparency and trust in AI-driven diagnostic tools. Mammography, as the cornerstone of early breast cancer detection, holds immense potential for improving outcomes when integrated with AI solutions. However, widespread adoption of AI in clinical settings depends on explainability, which enhances clinicians' confidence in these tools. By exploring various XAI techniques and evaluating their strengths and weaknesses, researchers can significantly advance precision medicine. This review synthesizes existing research on XAI in medical imaging, focusing on mammography, a domain often overlooked in XAI studies. It provides a comparative analysis of XAI techniques employed in mammography, assessing their diagnostic efficacy and identifying research gaps, such as the lack of specialized evaluation frameworks. Additionally, the review examines evaluation methods for XAI in medical imaging and proposes modifications tailored to mammography diagnostics. Insights from XAI advancements in other fields are also explored for their potential to enhance interpretability and clinical relevance in breast cancer detection. The study concludes by highlighting critical research gaps and proposing directions for developing reliable, effective AI models that integrate XAI to transform breast cancer diagnostics.