Integrating Generative AI in Youth Mental Healthcare Solutions
DOI:
https://doi.org/10.47611/jsrhs.v13i4.8202Keywords:
Artificial Intelligence, Generative AI, GenAI, Mental Health, Integration, Large Language Model, Technology, LLM, Open Source, Natural Language Processing, Impact, Challenges, Barriers, FutureAbstract
The advent of generative AI has opened up more avenues for mental health challenges among youth worldwide. This work represents a comprehensive survey of the recent progress of generative AI in youth mental healthcare concerning their impact, enabling technologies, challenges, and future directions. Some of the key technologies that form the bedrock for many applications involve NLP, transformer models, GAN, VAE, reinforcement learning, and Affective AI in the development of AI-powered chatbots, virtual therapists, personalized interventions, and early detection. We assess the role of open-source technologies such as orchestration frameworks, vector databases, large and small language models, front-end tools, and other enablers that empower RAG systems to elevate the capability for contextual relevance and effectiveness in Generation AI solutions. We present the positive impacts of generative AI: enhanced accessibility, increased engagement, and decreased cost, while also discussing critical concerns on ethical issues, data privacy, bias, and informed consent. We also probe barriers to mainstreaming, including technical limitations, policy gaps, and social resistance, and presented a critical review of pathways toward surmounting the difficulties. In the final portion of the study, we make conclusions and recommendations with regard to strategic future research and policy development recommendations that will ensure that generative AI is safe, ethical, and effective to deploy within youth mental healthcare for improved mental health outcomes among young populations globally.
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