The Impact of Artificial Intelligence in Identifying Stroke Risk Factors

Abstract

The application of artificial intelligence (AI) in the field of adolescent mental health care has demonstrated an encouraging prospect of early diagnosis, forecasting risks, and individualized therapy. According to recent research, machine learning, deep learning, and multimodal AI strategies have been used to predict psychiatric disorders, including depression, anxiety, and internalizing disorders. The AI-based solutions will depend on a wide range of data sources, such as electronic health records, wearable devices, social media activity, voice or facial recognition to determine mental health status objectively. These methods improve predictive validity, allow timely interventions and assist clinicians in decision making and overcome the constraints of conventional assessments. Nevertheless, there are still problems related to the small size or cultural bias of the dataset, privacy issues, and the lack of standard evaluations framework. Scoping reviews note the significance of the multimodal AI systems where behavioral, physiological, and digital biomarkers are merged to enhance robustness and generalizability. Moreover, AI applications are spread to workflow optimization of the healthcare sector, tracking treatment progress, and enhancing interaction with the patient. All in all, AI-inspired mental health technologies provide scalable, accurate, and proactive solutions to the well-being of adolescents, yet further studies are needed to make it ethical, equitable, and effective.

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