# An Overview of Tools and Technologies for Anxiety and Depression Management Using AI
## 2 Background: The Role of Informatics and AI in Mental Health  
Information and machine learning (ML) tools have been a powerful force in recent years, fundamentally changing how people, communities, and organizations tackle mental health issues. Technology integration not only affects projects, but also brings novel methods to raise awareness, diminish stigma, and enhance support for individuals with mental health challenges [10]. The proliferation of mobile applications specifically designed for mental health has significantly transformed how individuals manage their overall well-being. These applications provide customized resources for self-reflection, self-control, and emotional assistance, ranging from mood tracking to guided meditation [11]. Additionally, many of these applications leverage artificial intelligence (AI) to offer personalized treatments, enhancing mental health services by broadening their accessibility and reducing the need for physician intervention (18). AI, integrated into digital mental health tools, is particularly beneficial for providing self-help and guided cognitive behavioral therapy to those experiencing anxiety and depression [12]. Interactive AI has the potential to offer real-time management and treatment for mental health conditions. In some cases, it can supplement or even replace outdated, overburdened, or insufficient mental health systems [2]. However, several obstacles remain in the adoption of AI for mental health, such as accessibility, efficacy, reliability, user-friendliness, safety, ethical concerns, education, and cultural adaptation. Emerging technologies such as AI applications, real-time machine learning techniques, immersion technologies, and digital phenotyping--collecting real-time behavioral and mental health data from personal devices like smartphones and wearables--offer promising opportunities in this field. Within a broader framework, there is a recognized need for improved efficiency and speed in task management, as well as greater integration of machine interaction and automation. Moreover, rigorous evaluations of effectiveness and the incorporation of mixed, hybrid, or tiered care models are necessary to complement traditional mental health strategies [11]. Modeling human-machine interaction can further improve user-friendly app designs and help address disparities in mental health treatment, suicide prevention, and the development of digital therapeutic alliances [7]. Machine learning, a subset of AI, has become a significant catalyst for advancements in health care, particularly in mental health. Machine learning involves computers acquiring knowledge and improving their performance without being explicitly programmed [12]. By harnessing the growth in computing power and the availability of vast datasets, machine learning can derive valuable insights to aid in clinical decision making regarding diagnosis and treatment. This improves the delivery of individualized care to patients [13]. In the context of mental health, machine learning is particularly useful in identifying patient profiles that are more likely to respond positively to specific treatments, such as cognitive behavioral therapy [14]. By analyzing large patient datasets, machine learning tools can help clinicians make more accurate predictions and tailor treatments, accordingly improving outcomes and providing more comprehensive assistance. One of the key challenges faced by clinicians in diagnosing and treating mental health disorders is the subjective nature of traditional assessments, which often rely on brief interactions with patients and self-reported questionnaires. These methods offer only a snapshot of a patient's mental state, despite the dynamic nature of mood disorders [15]. AI offers the potential to enhance psychiatric assessments by integrating more objective techniques, such as audio and video analysis, which could improve diagnostic accuracy.