The integration of artificial intelligence (AI) into mobile applications has significantly transformed various domains, enhancing user experiences and providing personalized services through advanced machine learning (ML) and deep learning (DL) technologies. AI-driven mobile apps typically refer to applications that leverage ML/DL technologies to perform key tasks such as image recognition and natural language processing. In this paper, we conducted the most extensive empirical study on AI applications, exploring on-device ML apps, on-device DL apps, and AI service-supported (cloud-based) apps. Our study encompasses 56,682 real-world AI applications, focusing on three crucial perspectives: 1) Application analysis, where we analyze the popularity of AI apps and investigate the update states of AI apps; 2) Framework and model analysis, where we analyze AI framework usage and AI model protection; 3) User analysis, where we examine user privacy protection and user review attitudes. Our study has strong implications for AI app developers, users, and AI R\&D. On one hand, our findings highlight the growing trend of AI integration in mobile applications, demonstrating the widespread adoption of various AI frameworks and models. On the other hand, our findings emphasize the need for robust model protection to enhance app security. Additionally, our study highlights the importance of user privacy and presents user attitudes towards the AI technologies utilized in current AI apps. We provide our AI app dataset (currently the most extensive AI app dataset) as an open-source resource for future research on AI technologies utilized in mobile applications.

翻译：人工智能（AI）与移动应用的融合已显著改变了多个领域，通过先进的机器学习（ML）和深度学习（DL）技术提升了用户体验并提供了个性化服务。AI驱动的移动应用通常指利用ML/DL技术执行关键任务（如图像识别和自然语言处理）的应用程序。本文开展了迄今为止最广泛的AI应用实证研究，探索了设备端ML应用、设备端DL应用以及AI服务支持（基于云端）的应用。我们的研究涵盖了56,682个真实世界的AI应用，聚焦于三个关键视角：1）应用分析，我们分析了AI应用的流行度并调查了AI应用的更新状态；2）框架与模型分析，我们分析了AI框架的使用情况和AI模型保护；3）用户分析，我们考察了用户隐私保护和用户评论态度。我们的研究对AI应用开发者、用户以及AI研发具有重要启示。一方面，我们的发现突显了AI在移动应用中集成日益增长的趋势，展示了各种AI框架和模型的广泛采用。另一方面，我们的发现强调了增强应用安全性的鲁棒模型保护的必要性。此外，我们的研究强调了用户隐私的重要性，并呈现了用户对当前AI应用中使用的AI技术的态度。我们将AI应用数据集（目前最广泛的AI应用数据集）作为开源资源提供，以供未来对移动应用中使用的AI技术进行研究。