Paper Info
Title
Medkiosk: An Embodied Conversational Intelligence Via Deep Learning
Abstract
Today, rapid advancement and innovation of technology has changed the perspective and the way information precedes. The utilization of self-service interactive kiosks through the concept of touch screens have been becoming a massive trend used intensively, especially in e-commerce and medical healthcare. Thus, this research will investigate and innovate the current interactive kiosk to provide immediate responses and reliable information incorporating an intelligent conversational agent (CA). A CA also widely known as chatbots is a computer program to simulate the conversations between human and machine. Our goal is to design and develop a framework for revolutionizing medical kiosk via the incorporation of an intelligent chatbots. This research intends to escalate the productivity and efficiency in medical institutions by offering the capabilities to provide immediate reply as well as initiating a conversation, similar to conversing with the experienced customer service assistant. Latest innovations in natural language processing (NLP), machine learning and deep learning in the field of artificial intelligence (AI) ensure smarter decision making in providing accurate, reliable and up-to-date information. The future MedKiosks is reliant on the AI, specifically the machine learning and deep learning to unveil the correlations, algorithms, patterns and anomalies to improve chatbots knowledge base. Moreover the experiment would be hosted by positioning the MedKiosk in the hospital for real-time data collections.
Year
Venue
Keywords
2017
2017 13TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (ICNC-FSKD)
medical kiosk, artificial intelligence, intelligent conversational agent (CA), medical
Field
DocType
Citations 
Conversation,Customer service,Computer science,Embodied cognition,Human–computer interaction,Dialog system,Artificial intelligence,Computer program,Deep learning,Knowledge base,Interactive kiosk,Machine learning
Conference
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Pui Huang Leong100.34
Ong-sing Goh2298.45
yogan jaya kumar322.48