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）与移动应用的深度融合已显著改变多个领域，通过先进的机器学习（ML）和深度学习（DL）技术增强用户体验并提供个性化服务。AI驱动移动应用通常指利用ML/DL技术执行图像识别、自然语言处理等关键任务的应用。本文对AI应用进行了迄今最广泛的实证研究，涵盖设备端ML应用、设备端DL应用及AI服务支撑（云端）应用三大类别。我们的研究包含56,682个真实世界AI应用，聚焦三个关键维度：1）应用分析——分析AI应用的流行度及更新状态；2）框架与模型分析——分析AI框架使用情况及AI模型保护机制；3）用户分析——考察用户隐私保护与用户评论态度。本研究对AI应用开发者、用户及AI研发具有重要启示：一方面，我们的发现揭示了移动应用中AI集成日益增长的趋势，展示了各类AI框架与模型的广泛采用；另一方面，研究结果强调了加强模型保护以提升应用安全性的必要性。此外，本研究凸显了用户隐私的重要性，并呈现了用户对当前AI应用中所用技术的态度。我们提供的AI应用数据集（目前规模最大的AI应用数据集）将作为开源资源，供未来移动应用AI技术研究使用。