The medical field is one of the important fields in the application of artificial intelligence technology. With the explosive growth and diversification of medical data, as well as the continuous improvement of medical needs and challenges, artificial intelligence technology is playing an increasingly important role in the medical field. Artificial intelligence technologies represented by computer vision, natural language processing, and machine learning have been widely penetrated into diverse scenarios such as medical imaging, health management, medical information, and drug research and development, and have become an important driving force for improving the level and quality of medical services.The article explores the transformative potential of generative AI in medical imaging, emphasizing its ability to generate syntheticACM-2 data, enhance images, aid in anomaly detection, and facilitate image-to-image translation. Despite challenges like model complexity, the applications of generative models in healthcare, including Med-PaLM 2 technology, show promising results. By addressing limitations in dataset size and diversity, these models contribute to more accurate diagnoses and improved patient outcomes. However, ethical considerations and collaboration among stakeholders are essential for responsible implementation. Through experiments leveraging GANs to augment brain tumor MRI datasets, the study demonstrates how generative AI can enhance image quality and diversity, ultimately advancing medical diagnostics and patient care.

翻译：医学领域是人工智能技术应用的重要领域之一。随着医疗数据的爆炸式增长与多样化发展，以及医疗需求与挑战的持续升级，人工智能技术在医疗领域发挥着日益重要的作用。以计算机视觉、自然语言处理和机器学习为代表的人工智能技术已广泛渗透至医学影像、健康管理、医疗信息、药物研发等多元场景，成为提升医疗服务水平与质量的重要驱动力。本文探讨了生成式人工智能在医学影像领域的变革潜力，重点强调其生成合成ACM-2数据、增强图像质量、辅助异常检测及促进图像间转换的能力。尽管面临模型复杂度等挑战，生成模型在医疗保健领域的应用（包括Med-PaLM 2技术）已展现出良好效果。通过解决数据集规模与多样性不足的问题，这些模型有助于提升诊断准确率并改善患者预后。然而，负责任的实施需要审慎考量伦理问题并促进利益相关方协作。本研究通过利用生成对抗网络(GANs)扩充脑肿瘤MRI数据集的实验，展示了生成式人工智能如何增强图像质量与多样性，最终推动医学诊断与患者护理的进步。