Breast cancer (BC) stands as one of the most common malignancies affecting
women worldwide, necessitating advancements in diagnostic methodologies for
better clinical outcomes. This article provides a comprehensive exploration of
the application of Explainable Artificial Intelligence (XAI) techniques in the
detection and diagnosis of breast cancer. As Artificial Intelligence (AI)
technologies continue to permeate the healthcare sector, particularly in
oncology, the need for transparent and interpretable models becomes imperative
to enhance clinical decision-making and patient care. This review discusses the
integration of various XAI approaches, such as SHAP, LIME, Grad-CAM, and
others, with machine learning and deep learning models utilized in breast
cancer detection and classification. By investigating the modalities of breast
cancer datasets, including mammograms, ultrasounds and their processing with
AI, the paper highlights how XAI can lead to more accurate diagnoses and
personalized treatment plans. It also examines the challenges in implementing
these techniques and the importance of developing standardized metrics for
evaluating XAI's effectiveness in clinical settings. Through detailed analysis
and discussion, this article aims to highlight the potential of XAI in bridging
the gap between complex AI models and practical healthcare applications,
thereby fostering trust and understanding among medical professionals and
improving patient outcomes.

乳腺癌是全球妇女最常见的恶性肿瘤之一，需要改进诊断方法以获得更好的临床结果。本文全面探讨了可解释人工智能（XAI）技术在乳腺癌检测和诊断中的应用，通过与机器学习和深度学习模型的整合，尤其是与 SHAP、LIME、Grad-CAM 等 XAI 方法，分析乳腺癌数据集及其 AI 处理方式，强调 XAI 可以提高精确诊断和个性化治疗方案，讨论了实施这些技术的挑战，以及在临床环境中评估 XAI 有效性的标准化指标的重要性。该文章通过详细分析和讨论旨在凸显 XAI 在复杂 AI 模型和实际医疗应用之间搭建桥梁的潜力，从而建立医疗专业人员之间的信任和理解，改善患者的预后效果。

乳腺癌诊断：可解释人工智能（XAI）技术的全面探索

Breast Cancer Diagnosis: A Comprehensive Exploration of Explainable  Artificial Intelligence (XAI) Techniques

eXplainable Artificial Intelligence (XAI) is now an important and essential
part of machine learning, allowing to explain the predictions of complex
models. XAI is especially required in risky applications, particularly in
health care, where human lives depend on the decisions of AI systems. One area
of medical research is age prediction and identification of biomarkers of aging
and age-related diseases. However, the role of XAI in the age prediction task
has not previously been explored directly. In this review, we discuss the
application of XAI approaches to age prediction tasks. We give a systematic
review of the works organized by body systems, and discuss the benefits of XAI
in medical applications and, in particular, in the age prediction domain.

eXplainable Artificial Intelligence (XAI) is applied to age prediction tasks in medical research, providing explanations for predictions and biomarkers of aging and age-related diseases, demonstrating the benefits of XAI in medical applications.