###### Abstract
Furthermore, immersive technologies, like virtual reality (VR) and augmented reality (AR), are increasingly being integrated into therapeutic approaches, offering innovative solutions across various medical and psychological conditions [22; 25]. For example, during the post-COVID-19 era, VR telemedicine has shown positive results in improving treatment outcomes for patients with post-traumatic stress disorder (PTSD), especially when combined with cognitive behavioral therapy [26]. A meta-analysis assessed the performance of wearable AI in detecting anxiety, demonstrating promising accuracy and specificity in the early detection of anxiety disorders using wearable devices [27]. In addition to improving treatment outcomes, VR environments can be designed to display visual settings that reduce physiological stress, supporting faster recovery times. For instance, research has demonstrated that calming virtual environments, such as those simulating nature views, can significantly reduce stress in patients, potentially accelerating recovery times [28]. This concept is further supported by [PERSON]'s research, which demonstrated that natural views could enhance patient recovery post-surgery, providing further rationale for using VR to create stress-relieving environments in clinical settings [29]. By combining AI, VR, and wearable technologies, mental healthcare can benefit from a holistic, integrated approach that supports both psychological and physiological recovery. These tools provide the ability to monitor patients in real time, offer personalized therapeutic interventions, and reduce stress through calming visual environments. However, the successful deployment of these digitalized technologies will require overcoming challenges such as data privacy, infrastructure limitations, and patient compliance. Ensuring that these barriers are addressed will be essential for the wider adoption and success of AI and VR in healthcare. These applications of VR and AR provide a more supportive and immersive environment for patients, facilitating both psychological and physiological recovery. Whether used to augment cognitive therapies or to create stress-reducing settings, these digitalized technologies offer new opportunities for improving patient care and treatment outcomes across a wide range of medical conditions. Building on this, the integration of passive sensing data with AI models enables continuous, unobtrusive monitoring of schizophrenia patients, enhancing the ability to detect subtle behavioral changes that may precede a psychotic relapse. However, ensuring that sufficient data are obtained from passive sensing remains a key challenge. Data collection relies on the continuous use of devices and patient compliance, which can vary. Additionally, it is crucial to ensure that these data are representative of different stages of the patient's condition to build effective AI models. This approach not only improves early detection but also allows for personalized, proactive interventions, reducing the severity and frequency of relapse episodes [30]. To overcome these challenges, AI-driven systems must be designed to manage incomplete or noisy data, potentially using advanced imputation techniques or combining data from multiple sources to create a more comprehensive dataset. Leveraging AI models to analyze passive sensing data enables healthcare providers to tailor timely treatments to individual patient patterns, ensuring interventions address each patient's unique symptoms and behaviors [31]. This personalized approach is especially beneficial for schizophrenia, where symptom variability is common and traditional methods may fall short. Continuous monitoring reduces reliance on potentially inaccurate self-reported data due to cognitive impairments in schizophrenia patients [32]. Instead, AI-driven systems offer objective, real-time insights for a more precise understanding of the patient's condition.