# An Overview of Tools and Technologies for Anxiety and Depression Management Using AI
## 4 Results of Overview
### AI Methodologies and Tools Utilized for Anxiety and Depression Management  
AI tools for anxiety and depression encompass many technologies specifically developed to evaluate, control, and address mental diseases. Examples of such technologies include chatbots and virtual assistants, mood tracking applications, cognitive behavioral therapy apps, and virtual reality (VR) therapies [21]. AI-driven chatbots and virtual assistants utilize user engagement to provide emotional support, education, and coping mechanisms for managing symptoms associated with anxiety and depression [14]. These technologies employ natural language processing (NLP) algorithms to comprehend user data and deliver suitable replies. Conversely, these applications track users' emotional condition, symptoms, factors, and actions associated with anxiety and depression. Consequently, these insights offer valuable information on recurring patterns and long-term trends, enabling users to make well-informed choices regarding their mental well-being [4]. However, AI-driven CBT programs offer scientifically supported therapy approaches for anxiety and depression, including cognitive restructuring, behavioral activation, and relaxation strategies. These applications have the ability to include interactive activities, modules that may be completed independently, and customized feedback depending onthe user's input and tracking of their progress [26]. Moreover, VR-based therapies provide lifelike surroundings and scenarios to expose patients to stressful events or teach them relaxation and coping skills in a controlled setting. AI algorithms have the ability to tailor the virtual reality experience in real time by analyzing user emotions and physiological data. This enhances the effectiveness of treatments and allows for a more personalized experience [9]. AI-powered algorithms evaluate physiological data obtained from mobile devices, including heart rate variability, sleep patterns, activity levels, and speech patterns, to detect small changes that may indicate symptoms of anxiety and sadness [11]. Digital biomarkers provide precise and unbiased assessments of an individual's mental health condition and their reaction to therapy, enabling prompt intervention and tailored health care. By utilizing AI ML tools in these several domains, mental health practitioners may enhance the precision of assessments, the efficacy of treatments, and the involvement of patients in the management of anxiety and depression (Table 1). Figure 2 illustrates the distribution of mental health chatbots by category, categorizing the different types of mental health chatbots based on their functionality. CBT Chatbots make up the largest share (33.3%), indicating a high demand for therapeutic chatbots that use evidence-based techniques. The other three categories--Virtual Companions, AI Mental Health Assistants, and AI-Powered Virtual Therapists--are evenly distributed, each representing 22.2% of the total. This suggests that these different functionalities are also crucial components of the mental health chatbot landscape. Figure 3 illustrates the distribution of mental health applications by category, showing how various types of applications contribute to the overall landscape of mental health tools. The chart is divided into multiple segments, each representing a specific category of mental health application. AI-Driven Self-Reflection Platforms, Interactive Activities for Mental Well-being, and AI-Powered Mental Health Workouts are the largest categories, each taking up 11.8% of the chart. This indicates a growing focus on interactive, AI-powered, and self-guided tools for mental health care. Other categories like CBT Tools, Mindfulness and Meditation Software, Version: 4.158.0 (as of 2024) and Mental Well-being Tracking each contribute smaller but equally important shares (5.9%) to the overall ecosystem of mental health applications.