AI and Patient Convenience: Usage of AI-Based Medical Chatbots for Medical Diagnosis via Smartphones

This paper presents a theoretical model through which medical chatbot can be developed which works using the user’s health data gained from his heartbeat, which is processed using deep learning and machine learning analysis, based on a large database record of heartbeat sounds of a large sample of patients along with cross referencing and analysis of published medical literature data for arriving at a more accurate diagnosis of a person’s health from his heartbeat. Majority of the medical chatbot apps make use of machine learning via countless iterations from other patients in order to deliver more useful responses to their users. There are a few chatbots which make use of a body of medical literature for refining their responses but there are not many chatbot apps which make use of deep learning in addition to the above for delivering an even greater accurate diagnosis about the user’s state of health, which is a significant gap in the body of literature related to this topic. The theoretical model suggested in this paper makes an attempt for addressing that gap, leading to more accurate chatbots being made in the days ahead. The research is useful as the heart, by virtue of being a critical organ can be used to gauge a person’s state of health similar to a stethoscope and can lead to better suggestions for improving the user’s health.