Health-Bot: AI-Driven Textual Question Answering for Medical Applications

In the contemporary healthcare landscape, the imperative to deliver accessible, efficient, and affordable services is increasingly critical. A Chabot/dialogue system is a computer program which holds a conversation via auditory or textual ways. Such programs are specifically designed to provide the user a clone of how a human will chat and thereby it acts as a conversational partner. In this paper, we move beyond the notion of conventional Chabots towards developing a dialogue system for automatic medical diagnosis that converses with patients to collect additional symptoms beyond their self-reports and automatically makes a diagnosis. This research paper introduces Med-Bot, an advanced conversational Chabot designed to transform patient interaction and preliminary diagnosis through the use of Natural Language Processing (NLP). Med-Bot leverages a comprehensive medical knowledge graph and decision tree algorithms to effectively predict diseases based on symptom analysis. Initially, when a user reports symptoms, Med-Bot performs a targeted symptom analysis to ascertain the primary health concerns. It then utilizes probability analysis to dynamically generate follow-up questions, refining the context and precision of the user’s health status. The decision tree model, central to Med-Bot’s predictive capabilities, processes these inputs to identify and suggest potential diagnoses. This paper demonstrates how Med-Bot significantly reduces healthcare costs and enhances service accessibility, making it a pivotal development in digital healthcare tools.