A Framework for Chatbots in Medical Pre-Diagnosis

Abstract

Conversational AI systems or bots are a software framework designed to simulate human conversations. They interact with users, understand their needs and preferences, and recommend the next-best action with minimal human intervention. The core function of chatbots is to give the best response to any query that it receives. It shall answer sender’s questions, providing the most relevant information, asking follow-up questions, and making the conversation as realistic as possible. For accomplishing the ”realism” goal, these bots need to understand the intentions, sentiment, or underlying emotion of the sender’s message, and determine the most appropriate response. The bots may use metadata, such as speaker identity, preferences, or emotional state to provide relevant, grammatically correct response. Sometimes, sentiment analysis allows the Chatbot to ’understand’ the user’s mood by analyzing verbal and sentence structuring clues. Bots are common in HR, IT help desk, hospitality, and self-service applications with various levels of sophistication. Currently, these chatbots are a little rigid and underperform in-person counselling which are required in medical pre-diagnosis. However, one of the areas ripe with opportunities is in Healthcare Dialog systems. They have significant impact in democratizing the latest medical interventions globally with scale. In this paper, we will identify key functional requirements for a practical bot, particularly for pre-diagnosis based on the symptom extraction, extract the most appropriate and suggest a framework for achieving some of the functionality with algorithms.