Youth mental health-related problems and disorders have garnered increased attention due to global prevalence estimates that have, in some cases, increased following the COVID-19 pandemic. Various methodologies have been proposed to leverage artificial intelligence (AI) for detecting mental health problems in the general population; however, research specifically focused on AI methods for youth remains limited. Shortcomings in modern AI include limited training data modalities (i.e., types of input data used for model training), reliance on offline training, and the use of static models. This scoping review provides an overview of evidence that uses AI methods applied to youth mental health (YMH) and provides an assessment of the current state of research that integrates multimodal AI (i.e., models that incorporate multiple data modalities) and/or online learning (i.e., incremental or continual model training from streaming data) for the diagnosis, monitoring, and treatment of YMH-related problems. The findings indicate that research in AI applied to YMH is limited in the areas of multimodal AI and online learning. The number of studies in this field is steadily growing. Studies incorporating online learning demonstrate that this approach enhances model performance and adaptability, which is crucial for developing translational models capable of addressing real-world challenges effectively. Despite these advances, key challenges remain, including the availability and long-term validity of multimodal data, the lack of participant-related information in certain databases and studies, the ethical and logistical difficulties of collecting data from minors, and the computational costs of training robust AI models.
Campos et al. (Sat,) studied this question.