# AI Chatots for Mental Health: A Scoping Review of Effectiveness, Feasibility, and Applications
### Technical Background
The challenge lies in creating models that can seamlessly integrate and interpret these diverse data types, ensuring consistent and reliable outputs. Using multimodal data, we can now develop Large Language Models (LLMs) [42] that employ deep learning techniques to handle various types of data for numerous NLP tasks, such as recognition, translation, content generation, and text creation. ChatGPT-4, where GPT stands for Generative Pre-trained Transformer, exemplifies the advanced AI chatbots based on this technology. Training an LLM for specific tasks requires significant hardware resources and extensive data, making the creation of custom AI chatbot models costly [43]. Transfer learning provides an effective solution to this challenge. By starting with a pre-trained model like GPT, we can fine-tune it for specific tasks using targeted data. This fine-tuning process enhances the LLM's capabilities, making it more accurate and reliable for particular applications [43; 44; 45]. Figure 1 illustrates a generic fine-tuning operation in the medical domain. In this context, the resulting model can also be customized to process various inputs, including text and multimedia content such as images, videos, and audio. For chatbots to be effective in large-scale mental health interventions, they must be both scalable and robust [29; 30]. Cloud computing and distributed systems are crucial for managing large volumes of concurrent interactions. Research into efficient data processing and storage solutions, as well as load balancing algorithms, is essential to ensure chatbots can operate effectively at scale. Furthermore, maintaining the reliability and uptime of these systems is critical to sustaining user trust and engagement. Recent advances in LLMs like GPT-4 and Med-PaLM-2 have shown impressive capabilities across medical domains [46]. However, evaluating their performance specifically in mental health applications has been lacking [47; 48]. Several studies have begun exploring LLMs' potential in mental health tasks through benchmarking and extensive evaluations. [PERSON] et al. [49] found that while zero-shot and few-shot prompting showed limited performance, instruction fine-tuning significantly boosted LLM accuracy, with their Mental-Alpaca and Mental-FLAN-T5 models outperforming much larger models like GPT-3.5 and GPT-4. [PERSON] et al. [50] introduced the first multi-dimensional mental health benchmark, revealing significant room for LLM improvement in this domain. [PERSON] et al. [51] evaluated LLMs on cognitive distortion and suicide risk classification for Chinese social media, highlighting GPT-4's strong performance. As LLMs integrate further into mental health care [52], a dedicated, rigorous benchmark focused specifically on mental health is crucial. Such a benchmark could evaluate understanding psychological expressions, providing support resources, avoiding harmful outputs, and other key capabilities [49; 50]. Further identifying LLMs' strengths and weaknesses in this area could drive the development of more robust, reliable, and ethical models tailored for assisting those with mental health needs [48; 52].