Mindease: A Mental Health Chatbot for Personalized Support

Mental health disorders such as depression, anxiety, and stress-related conditions have become increasingly prevalent due to rapid lifestyle changes, academic pressure, and social isolation. Despite the growing demand for mental health support, access to professional care remains limited due to social stigma, high costs, and insufficient availability of trained professionals. Artificial Intelligence (AI)–based conversational agents, commonly known as chatbots, have emerged as promising tools to bridge this gap by providing scalable, accessible, and confidential mental health assistance. This review paper presents a comprehensive analysis of AI-based mental health chatbots, focusing on their architecture, underlying Artificial Intelligence and Natural Language Processing (NLP) techniques, applications, and limitations. Existing literature is systematically examined to understand the evolution of mental health chatbots, ranging from rule-based systems to advanced machine learning and deep learning models. The review also highlights commonly used NLP techniques such as sentiment analysis, intent recognition, and contextual response generation. Furthermore, the paper discusses the benefits and challenges associated with deploying AI-driven mental health chatbots, including ethical concerns, data privacy, emotional accuracy, and limitations in handling complex psychological conditions. Finally, future research directions are proposed to improve chatbot empathy, personalization, and clinical reliability. This review aims to provide researchers and developers with a structured understanding of current advancements and future possibilities in AI-driven mental health support systems.