# AI Chatots for Mental Health: A Scoping Review of Effectiveness, Feasibility, and Applications
### Technical Background  
Natural Language Processing and Detailed Aspects of AI Chatbots  
The effectiveness and innovation of AI chatbots in mental health interventions heavily depend on advancements and methodologies within computer science [29; 30]. A chatbot is essentially a computer program designed to interact with users, addressing specific tasks or requests through AI technologies. Key to these technologies are the machine learning (ML) and deep learning (DL) techniques, which, when integrated with NLP, form the foundation of AI chatbots [31]. NLP is crucial to their functionality, allowing machines to comprehend and generate human language. This combination enables chatbots to understand and interpret user inputs effectively [32; 33] (e.g., text, audio [34]). These modern technologies are used in different industries like healthcare to collect, for example, patient data and to provide health education. It is possible to distinguish three main categories of AI chatbots [35; 36; 37]:  
* Menu/button-based chatbots: Use buttons and menus, are common and simple, follow decision tree logic. Users make selections to receive answers. * Keyword recognition-based chatbots: use AI to identify and respond to specific keywords from user input. * Contextual chatbots: use AI and machine learning to understand user intentions and sentiments through technologies like voice recognition. Recent advancements in NLP, driven by deep learning models such as transformers, have greatly enhanced chatbots' capabilities in understanding context, sentiment, and nuances in user inputs. Despite these improvements, challenges persist in achieving seamless conversational flow, accurately interpreting user emotions, and comprehending colloquial or context-specific language [29; 30]. The key elements that characterize an AI chatbot are as follows [38; 39; 40]:  
1. Understanding: chatbots use NLP to comprehend user requests and human language complexities. 2. Contextual responses: NLP allows chatbots to provide relevant, context-aware answers. 3. Continuous learning: chatbots learn new language patterns from interactions, staying current with trends. Machine learning powers intelligent chatbot responses [31]. * Training data: chatbots learn from extensive conversational data to understand user questions and provide suitable answers. * Pattern recognition: chatbots identify patterns in user behavior and language to predict and generate accurate responses. * Feedback loop: machine learning continuously refines chatbot algorithms, improving accuracy with each interaction. Machine learning techniques enable chatbots to personalize interactions based on user data, tailoring responses to individual needs and preferences. Methods such as reinforcement learning and user feedback loops can optimize chatbot responses over time [31]. Developing adaptive learning models that dynamically adjust to changes in user behavior and mental state presents a promising area for exploration. Balancing data privacy with personalization is critical, requiring robust encryption and anonymization protocols. Training an AI chatbot involves several steps: collecting and preprocessing data, selecting a model architecture, training the model, evaluating and optimizing its performance, and deploying it [31]. Some models can continue to learn from real-time interactions through continuous learning [41]. This process involves gathering data during conversations, analyzing it, updating the model, and evaluating its performance. While not all AI chatbots use continuous learning, it can significantly enhance responsiveness and adaptability [40]. The decision to implement continuous learning depends on the specific needs of the project. Advanced chatbots can leverage multimodal data, including text, voice, and even facial expressions, to provide more holistic support. Integrating voice recognition and analysis can make interactions more natural, especially for users who may have difficulties with text-based communication [29; 30].