Exploring the Impact of Artificial Intelligence on Mental Health Interventions
Keywords:
mental health in young people, psychosis, depression, computational health, chatbots, sentiment analysisAbstract
Purpose: To keep the early clinical improvements from mental health treatments, longer-term intervention programs may be necessary. Nevertheless, this may not be doable because of how intense early intervention programs that include face-to-face interactions are. To avoid the intervention's advantages from eroding, it may be cost-effective and engaging to use internet-based treatments tailored to kids as an adjunct. Nevertheless, the delivery of therapeutic information in online interventions has traditionally been handled by human moderators. Customized online treatment cannot be informed without more advanced models that are sensitive to user data. Therefore, to reimagine online treatments for adolescent mental health, it is essential to combine user experience with advanced and innovative technology to provide information. The web application offers supervised social therapy. In this presentation, we will go over the key aspects of the system and talk about our ongoing projects including using AI and sophisticated computational approaches to make the system more user-friendly and better at finding and delivering therapeutic material.
Method/Findings: As a case study, They look at the ongoing Horizons site, which is a randomized controlled experiment that followed children as they recovered from early psychosis for five years. They are using MOST to power this experiment. They go over the background of the project, the main features, and how to utilize the web app. Along with highlighting some of the system's shortcomings, we go over some of the advancements made to the system, such as the inclusion of relevant use patterns. As a result, we are now driven to improve the system with new mechanisms for treatment material distribution and to increase user engagement via the application of computational and artificial intelligence approaches. To customize interventions and scale the system, we focus on how we have used chatbot technology and natural language analysis.
Recommendations/ Results from the many clinical studies conducted so far have confirmed the practicality of the novel MOST system. An essential next step in the advancement of the software system is to include sophisticated and automated content delivery techniques. This will allow for more data-driven possibilities, better analysis of use trends, and the possibility of large-scale deployment (Boucher et al., 2021)
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