TY - GEN
T1 - Addressing the Productivity Paradox in Healthcare with Retrieval Augmented Generative AI Chatbots
AU - Ranasinghe, Sajani
AU - De Silva, Daswin
AU - Mills, Nishan
AU - Alahakoon, Damminda
AU - Manic, Milos
AU - Lim, Yen
AU - Ranasinghe, Weranja
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Artificial Intelligence (AI) is reshaping the health-care landscape through diverse innovations, personalisations and decision-making capabilities. The human-like intelligence of Generative AI has been fundamental in driving this transformation across the sector. Despite large investments and some early successes, several studies have signalled the emergence of a productivity paradox due to inherent limitations of Generative AI that disintegrate within the complexity of healthcare systems and operations. In this study, we investigate the capabilities of Retrieval Augmented Generation (RAG) and Generative AI chatbots in addressing some of these challenges. We present the design and development of a Retrieval Augmented Generative AI Chatbot framework for consultation summaries, diagnostic insights, and emotional assessments of patients. We further demonstrate the technical value of this framework in service innovation, patient engagement and workflow efficiencies that collectively move to address the productivity paradox of AI in healthcare.
AB - Artificial Intelligence (AI) is reshaping the health-care landscape through diverse innovations, personalisations and decision-making capabilities. The human-like intelligence of Generative AI has been fundamental in driving this transformation across the sector. Despite large investments and some early successes, several studies have signalled the emergence of a productivity paradox due to inherent limitations of Generative AI that disintegrate within the complexity of healthcare systems and operations. In this study, we investigate the capabilities of Retrieval Augmented Generation (RAG) and Generative AI chatbots in addressing some of these challenges. We present the design and development of a Retrieval Augmented Generative AI Chatbot framework for consultation summaries, diagnostic insights, and emotional assessments of patients. We further demonstrate the technical value of this framework in service innovation, patient engagement and workflow efficiencies that collectively move to address the productivity paradox of AI in healthcare.
KW - Artifical Intelligence
KW - Chatbot
KW - Digital Health
KW - Generative AI
KW - Healthcare
KW - Productivity Paradox
KW - Retrieval Augmented Generation
UR - https://www.scopus.com/pages/publications/85195801950
U2 - 10.1109/ICIT58233.2024.10540818
DO - 10.1109/ICIT58233.2024.10540818
M3 - Conference contribution
AN - SCOPUS:85195801950
T3 - Proceedings of the IEEE International Conference on Industrial Technology
BT - ICIT 2024 - 2024 25th International Conference on Industrial Technology
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 25th IEEE International Conference on Industrial Technology, ICIT 2024
Y2 - 25 March 2024 through 27 March 2024
ER -