乳房X光检查的解读需要多年的培训和经验。目前，与其他诊断放射学一样，乳房X光检查培训是通过机构图书馆、书籍和长期积累的经验进行的。我们探索人工智能 (AI) 生成的图像是否有助于模拟教育，并在培训中显着提高住院医生的表现。我们开发了一种生成对抗网络 (GAN)，能够生成具有不同特征的乳房 X 线摄影图像，例如尺寸和密度，并创建了一个用户可以控制这些特性的工具。该工具允许用户（放射科住院医师）将癌症真实地插入乳房X线照片的不同区域。然后我们在培训中向居民提供了这个工具。居民被随机分为练习组和非练习组，并评估使用这种工具练习之前和之后的表现差异（与非练习组中没有干预相比）。 50 名居民参与了这项研究， 27 人接受了模拟训练，23 人没有接受模拟训练。敏感性（7.43％，p值 = 0.03时显着）、阴性预测值（5.05％，p值 = 0.008时显着）和准确性（6.49％，p值 = 0.01时显着）显着改善我们的研究展示了模拟训练在诊断放射学中的价值，并探索了生成式人工智能实现此类模拟训练的潜力。使用生成式人工智能，可以开发模拟训练模块，通过为住院医生提供各种不同病例的视觉印象来帮助他们进行培训。生成网络可以产生具有特定特征的诊断成像，这对于培训住院医生可能有用。生成图像的培训提高了住院医师的乳房X线摄影诊断能力。开发利用这些网络的类似游戏的界面可以在短时间内提高性能。© 2024。作者，获得欧洲放射学会的独家许可。

The interpretation of mammograms requires many years of training and experience. Currently, training in mammography, like the rest of diagnostic radiology, is through institutional libraries, books, and experience accumulated over time. We explore whether artificial Intelligence (AI)-generated images can help in simulation education and result in measurable improvement in performance of residents in training.We developed a generative adversarial network (GAN) that was capable of generating mammography images with varying characteristics, such as size and density, and created a tool with which a user could control these characteristics. The tool allowed the user (a radiology resident) to realistically insert cancers within different regions of the mammogram. We then provided this tool to residents in training. Residents were randomized into a practice group and a non-practice group, and the difference in performance before and after practice with such a tool (in comparison to no intervention in the non-practice group) was assessed.Fifty residents participated in the study, 27 underwent simulation training, and 23 did not. There was a significant improvement in the sensitivity (7.43 percent, significant at p-value = 0.03), negative predictive value (5.05 percent, significant at p-value = 0.008) and accuracy (6.49 percent, significant at p-value = 0.01) among residents in the detection of cancer on mammograms after simulation training.Our study shows the value of simulation training in diagnostic radiology and explores the potential of generative AI to enable such simulation training.Using generative artificial intelligence, simulation training modules can be developed that can help residents in training by providing them with a visual impression of a variety of different cases.Generative networks can produce diagnostic imaging with specific characteristics, potentially useful for training residents. Training with generating images improved residents' mammographic diagnostic abilities. Development of a game-like interface that exploits these networks can result in improvement in performance over a short training period.© 2024. The Author(s), under exclusive licence to European Society of Radiology.