Leveraging Artificial Intelligence for Mental Health: A Comprehensive Review of Techniques and Applications

Abstract

Artificial Intelligence has completely changed how mental health issues are addressed by creating new ways to improve patient outcomes, diagnosis, and treatment. The current research examine the AI driven solutions like virtual agents that are used for individualized therapeutic interventions, machine learning algorithms that predict mental health analytics, and natural language processing concerning early diagnosis. The study aims to address important issues like ethical dilemmas, scalability, and cross-cultural adaptation, as well as to present a novel AI-based model and examine the shortcomings of existing mental health models. Mixed method approaches, case studies, analytical frameworks, and library catalog searches are used to guarantee thorough evaluation within regions. The proposed approach aims to increase access and inclusion by utilizing multimodel data integration, reinforcement learning, and ethical AI design principles. According to comparative studies, this new approach shows promise in addressing significant issues with current models, including issues with data security, patient involvement, and economic inefficiency. There are many biases, implementation issues, and ethical conundrums, despite the positive aspects they offer. The paper’s conclusion suggests an ideal system architecture that would combine artificial intelligence with human oversight to provide scalable, long-lasting, and therapeutically effective mental health treatments.