Empowering Healthcare Communication: Exploring NLP-Enhanced Chatbots for Patient Engagement and Information Delivery

Abstract

The significance of healthcare technology cannot be overemphasized since with the timely availability of correct medical information, better patient care and outcomes can be achieved. Because more healthcare-related queries are posed in the digitalseeking form, there is a demand for intelligent systems offering relevant correct medical answers. This paper discusses the process of creating an automated information system for medical purposes intended to assist patients by offering trustworthy and individual responses to their questions about health. The center of the framework depends on AI procedures that explicitly influence Bidirectional Long Momentary Memory (BiLSTM) and Term Recurrence Backwards Report Recurrence (TF-IDF) to process and decipher enormous medical services datasets. As well as dealing with individual questions, the framework upholds different subqueries inside a solitary question and gives total responses by joining significant responses. This unique usefulness guarantees clients have a comprehensive comprehension of their questions and covers all parts of their clinical worries. Utilizing a clustering system, comparative questions are gathered, streamlining the framework’s capacity to recover predictable and exact responses. Utilizing TF-IDF with BiLSTM embeddings works on the correctness of question handling by catching the context oriented significance of client input, guaranteeing that even the most requesting inquiries are handled really. This work adds to the field of medical care innovation by introducing a creative and adaptable arrangement that works on quiet commitment. The framework further develops admittance to clinical data, yet additionally upholds informed independent direction, making it a significant instrument for present day medical services conditions.