## 1 Introduction  
Diabetes mellitus (DM) is a chronic condition that requires regular attention and concern for many aspects besides blood sugar levels [1]. Currently, several people are suffering from diabetes all over the world, and estimates suggest that this number will exceed 700 million globally before the year 2050 [2]. Diabetic foot ulcer (DFU) is one of the most common complications related to diabetes mellitus and one of the leading factors for mortality rates and healthcare costs [3]. According to worldwide estimates, up to 25% of people with diabetes are likely to develop a foot ulcer sometime in their lives [4]. Besides causing pain, such ulcers might also progress into severe infections and gangrene, culminating in lower limb amputation in many instances [5]. Traditional DFU care includes regular clinical evaluations, wound cleaning, and surgical interventions when necessary. However, these methods are often resource-intensive and require specialized expertise [6]. Additionally, the rapid progression of DFUs necessitates early diagnosis and intervention to prevent severe complications [7]. Recent advances in medical technologies, such as artificial intelligence (AI) [8] and generative AI [9], help address these challenges. AI refers to using computer systems to perform tasks that typically depend on human cognitive abilities, such as analyzing medical images. [PERSON] coined the term and defined it as \"the science and engineering of making intelligent machines, especially intelligent computer programs\" [10]. Generative AI, a subset of AI, focuses on creating new data based on existing patterns [11]. These advancements enhance diagnostic accuracy, optimize care processes, and improve patient outcomes. Several review studies have explored the application of AI in tasks related to DFU management [12], including classification, segmentation, detection, and predictive modeling. For instance, prior reviews highlight how convolutional neural networks (CNNs) are increasingly adopted for DFU classification [13; 14; 15]. Similarly, prior reviews have highlighted the use of state-of-the-art method models, including machine learning algorithms such as support vector machines (SVMs) [16; 17], and artificial neural networks (ANNs) [18; 19] to forecast DFU healing outcomes and amputation risks. Segmentation techniques leveraging CNN architectures, such as UNet and LinkNet [20], and Double Encoder-ResUNet (DE-ResUNet) [21] have demonstrated significant progress in delineating DFU boundaries. Researchers have employed advanced DL architectures like EfficientNet [22] and DenseNet [23] to enhance the detection of DFUs in clinical and telemedicine settings. They have utilized models such as conditional generative adversarial networks (cGANs) [24] and diffusion models [25] to generate synthetic datasets for DFU research in the context of generative AI. While these reviews provide valuable insights into advancements in technical innovations, they predominantly focus on algorithmic performance metrics, such as accuracy, precision, recall, and F1 scores, often overlooking the practical integration of AI into real-world applications, including mobile health technologies. However, several individual studies have highlighted the transformative potential of AI-powered smartphone applications; such solutions enable advancements in areas such as predictive healing, monitoring, wound localization, and detection, displaying the practical utility of these technologies in diverse clinical settings. To address this gap, this paper aims to review the integration of AI in DFU management, focusing on real-world applicability. The specific contributions of this paper are as follows: (i) It provides review of the integration of AI in the classification, prediction, and segmentation of DFUs. (ii) It explores the role of generative AI in enhancing data augmentation and addressing limitations in dataset availability. (iii) It conducts an examination of innovations in mobile application technologies for the monitoring and management of DFUs.