Dur Karasayar Ayşe Hümeyra, Kulaç İbrahim, Kapucuoğlu Nilgün
Graduate School of Health Sciences, Koç University Faculty of Medicine, İstanbul, Turkey.
Department of Pathology, Başakşehir Çam and Sakura Hospital, İstanbul, Turkey.
Eur J Breast Health. 2025 Mar 25;21(2):93-100. doi: 10.4274/ejbh.galenos.2025.2024-12-8. Epub 2025 Mar 3.
Artificial intelligence (AI) and digital pathology are transforming breast cancer management by addressing the limitations inherent in traditional histopathological methods. The application of machine learning algorithms has enhanced the ability of AI systems to classify breast cancer subtypes, grade tumors, and quantify key biomarkers, thereby improving diagnostic accuracy and prognostic precision. Furthermore, AI-powered image analysis has demonstrated superiority in detecting lymph node metastases, contributing to more precise staging, treatment planning, and reduced evaluation time. The ability of AI to predict molecular markers, including human epidermal growth factor receptor 2 status, BRCA mutations and homologus recombination deficiency, offers substantial potential for the development of personalized treatment strategies. A collaborative approach between pathologists and AI systems is essential to fully harness the potential of this technology. Although AI provides automation and objective analysis, human expertise remains indispensable for the interpretation of results and clinical decision-making. This partnership is anticipated to transform breast cancer care by enhancing patient outcomes and optimizing treatment approaches.
人工智能（AI）和数字病理学正在通过解决传统组织病理学方法固有的局限性来改变乳腺癌的管理。机器学习算法的应用提高了人工智能系统对乳腺癌亚型进行分类、对肿瘤分级以及量化关键生物标志物的能力，从而提高了诊断准确性和预后精度。此外，人工智能驱动的图像分析在检测淋巴结转移方面已显示出优势，有助于更精确的分期、治疗规划并缩短评估时间。人工智能预测分子标志物的能力，包括人类表皮生长因子受体2状态、BRCA突变和同源重组缺陷，为个性化治疗策略的开发提供了巨大潜力。病理学家和人工智能系统之间的协作方法对于充分发挥这项技术的潜力至关重要。尽管人工智能提供了自动化和客观分析，但人类专业知识对于结果解释和临床决策仍然不可或缺。这种合作关系有望通过改善患者预后和优化治疗方法来改变乳腺癌护理。
