###### Abstract
Incorporating such a system would not only enhance the quality of care for individuals with schizophrenia but also alleviate some of the burdens on healthcare professionals and family caregivers [16]. By automating certain aspects of monitoring, remote systems would enable healthcare providers to focus their efforts on the most critical interventions while still maintaining a high level of oversight. Furthermore, they empower patients by giving them an active role in managing their condition, promoting greater autonomy, and potentially improving long-term outcomes. When combined with diagnostic tools, like the EEG, remote monitoring would provide a comprehensive, cost-effective solution for the holistic care of schizophrenia patients, addressing both their psychiatric needs and physical health concerns [17]. E-health technologies, including mobile devices and telepsychiatry, play a critical role in schizophrenia management by improving continuity of care, supporting medication adherence, and enabling remote monitoring and virtual consultations [18; 19]. These tools enhance patient outcomes by providing accessible care and boosting patient engagement. [PERSON] et al. (2016) emphasized the potential of digital technologies to promote independent living and medication adherence through reminders and virtual check-ins [18]. Wearable devices, equipped with sensors and connected to telemedicine platforms, provide real-time data, such as EEG readings and physiological signals [20]. AI algorithms analyze these datasets to detect early signs of schizophrenia, enabling proactive interventions that reduce episode severity. [PERSON] et al. (2021) also highlight the use of smartphone-based sensors for continuous monitoring, identifying behavioral changes that may signal impending relapses [21]. In recent years, Artificial Intelligence (AI) has seen remarkable growth, unlocking transformative potential across multiple sectors, including healthcare. AI's integration into mental healthcare has notably improved diagnostic accuracy, early detection, and treatment of mental health conditions. This integration has been particularly impactful through AI-powered chatbots and virtual therapists, offering stigma-free, personalized interventions based on real-time data analysis [22; 23]. One of AI's key contributions to mental health is personalized treatment. Addressing challenges related to data privacy, accessibility, and ethical concerns is essential for successful integration into mainstream mental healthcare. As AI advances, it holds the potential to revolutionize schizophrenia care with more effective and individualized treatment strategies. AI algorithms can analyze patient data from diverse sources, such as electronic health records (EHRs) and wearable devices, to recommend tailored interventions. While these data sources are valuable, the challenge of obtaining sufficient and high-quality data for analysis must be addressed. Collecting adequate data for AI models involves ensuring that data streams from various sources are reliable, comprehensive, and representative of the diverse patient population. For instance, wearable devices and telepsychiatry provide continuous monitoring and generate significant amounts of real-time data, but they must be carefully integrated into healthcare workflows to avoid gaps in coverage or biases in the data collected. The combination of AI with telepsychiatry and wearable technologiesfurther supports mental healthcare by improving access to treatment and enhancing the personalization of care [24]. AI algorithms detect early signs of mental health conditions, and wearable devices continuously monitor mental well-being through sensors that track physiological data, such as sleep patterns and physical activity. Ensuring sufficient data from these devices requires active participation from patients and continued advancements in sensor technology, which can help capture more precise and relevant information.