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Abstract：
The advent of artificial intelligence (AI) represents a real game changer in today\'s landscape of breast cancer imaging. Several innovative AI-based tools have been developed and validated in recent years that promise to accelerate the goal of real patient-tailored management. Numerous studies confirm that proper integration of AI into existing clinical workflows could bring significant benefits to women, radiologists, and healthcare systems. The AI-based approach has proved particularly useful for developing new risk prediction models that integrate multi-data streams for planning individualized screening protocols. Furthermore, AI models could help radiologists in the pre-screening and lesion detection phase, increasing diagnostic accuracy, while reducing workload and complications related to overdiagnosis. Radiomics and radiogenomics approaches could extrapolate the so-called imaging signature of the tumor to plan a targeted treatment. The main challenges to the development of AI tools are the huge amounts of high-quality data required to train and validate these models and the need for a multidisciplinary team with solid machine-learning skills. The purpose of this article is to present a summary of the most important AI applications in breast cancer imaging, analyzing possible challenges and new perspectives related to the widespread adoption of these new tools.
摘要：
人工智能（AI）的出现代表了当今乳腺癌成像领域真正的游戏规则改变者。近年来，已经开发并验证了几种基于AI的创新工具，这些工具有望加速实现真正的患者量身定制管理的目标。许多研究证实，将人工智能适当地整合到现有的临床工作流程中可以为女性带来显著的好处。放射科医生,和医疗保健系统。事实证明，基于AI的方法对于开发新的风险预测模型特别有用，该模型集成了多个数据流以计划个性化的筛查协议。此外，人工智能模型可以帮助放射科医生在预筛查和病变检测阶段，提高诊断准确性，同时减少与过度诊断相关的工作量和并发症。放射组学和放射基因组学方法可以外推所谓的肿瘤成像特征以计划靶向治疗。人工智能工具开发的主要挑战是训练和验证这些模型所需的大量高质量数据，以及需要一个具有扎实机器学习技能的多学科团队。本文的目的是总结AI在乳腺癌成像中最重要的应用，分析与广泛采用这些新工具相关的可能挑战和新观点。
