## 2 Literature Review
### AI-Driven Innovations in Schizophrenia Diagnosis: Applying DL and EEG Data  
AI has emerged as a transformative tool in the detection and diagnosis of mental health disorders [49]. By analyzing various data types, including medical images, text, speech, facial expressions, and EHR data, AI is revolutionizing mental health diagnostics [50]. AI-based systems are advancing rapidly, offering enhanced capabilities in the early detection of mental health disorders. Building on the earlier discussions of AI-enabled RPM systems, it becomes evident that continuous monitoring is essential in managing chronic conditions, like schizophrenia. RPM systems provide an efficient, non-invasive platform for continuous EEG monitoring, which is vital for tracking the progression of the disease and detecting early signs of deterioration. The integration of AI in these systems further enhances their capabilities by enabling the analysis of EEG data, allowing for more precise diagnoses and personalized interventions. This progress improves both the accuracy and efficiency of schizophrenia diagnosis and management, enabling more prompt and precise interventions. In addition to enhancing the overall quality of care, it marks a significant breakthrough in managing the unpredictable nature of schizophrenia symptoms. Early detection of neural irregularities allows for quicker interventions, ultimately leading to better patient outcomes. Although the use of EEG signals as a diagnostic biomarker for schizophrenia is still being actively researched, early studies demonstrate promising results. DL models, particularly convolutional neural networks (CNNs) and hybrid DL frameworks, have shown strong potential in analyzing EEG data for schizophrenia classification. These advanced models are capable of capturing intricate neural patterns that are often overlooked by traditional methods, offering a more precise and scalable approach to diagnosing schizophrenia. By leveraging EEG data, which can be seamlessly collected through AI-enabled RPM systems, these technologieshave the potential to significantly enhance the early detection and continuous management of the disorder. While challenges such as overfitting and data dimensionality still need to be addressed, the integration of EEG-based AI models represents a promising step toward more effective and accessible schizophrenia diagnosis. SchizoGoogLeNet, a model that excels in autonomous feature extraction and is based on the GoogLeNet architecture, was introduced by [PERSON] et al. [51]. Compared to conventional techniques, this model offers remarkable accuracy and computational efficiency in capturing complex patterns in brain scans. The capacity of SchizGoogLeNet to differentiate between individuals with schizophrenia and HCs underscores the increasing significance of CNNs in the domain of neuropsychiatric diagnostics and the wider field of computational psychiatry. The use of CNNs for the diagnosis of schizophrenia advances previous research that used models, such as 3D-CNNs, for brain image classification. The highest performance is obtained by combining the suggested deep feature extraction model with a support vector machine (SVM), according to experimental data, which produces a 99.02% classification rate for SZ and an overall accuracy of 98.84%. EEG time series data were converted into images through an innovative method presented by [PERSON] and [PERSON] [17], further applying DL algorithms for classification. Their study demonstrated that converting these signals into spatial representations, through Recurrence Plot and Gramian Angular Field, improved the accuracy of detecting schizophrenia, providing an accuracy of 90% and 93.2%, respectively, leveraging deep CNNs to capture intricate temporal and spatial features. This approach represents a significant advancement over traditional EEG analysis methods, offering enhanced diagnostic precision and paving the way for more robust and scalable tools in mental health diagnostics. [PERSON] et al.