9th International Conference on ICT for Sustainable Development

Authors - Bhawani Singh Rathore, Sandeep Chaurasia
Abstract - The rising incidence of mental health issues makes it more important than ever to include technology-driven solutions into the field of mental health assistance. This study looks at how important it is to use Transformer-based language models and Recurrent Neural Networks (RNNs) to create healthcare chatbots that offer individualized mental health care. To help chatbots comprehend and interact with users in a more sympathetic and contextually relevant way, this paper investigates the synergistic use of these two AI approaches. This research highlights the value of individualized mental health care through a thorough literature analysis, underscoring the necessity for chatbots to adjust and develop in response to the unique requirements of each user. With the help of Transformer models and RNNs, these chatbots can provide responses that are human-like while also maintaining and managing the context of ongoing conversations. These effects include improving emotional intelligence, providing tailored solutions, and continually learning from user interactions. It highlights how important a role these chatbots might play in providing mental health help to a larger community. This survey paper offers a pilot study in which we cover both RNN and Transformer role in deployment of healthcare solution. The findings show that using RNNs and Transformers to provide individualized mental health care can have positive effects, since users report feeling more satisfied and that their treatment is effective.