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  • How Industry 4.0 and advanced manufacturing could help to reduce procedural caused medical waste?

Pranay, Arun Kumar and Wang, Stephen Jia, 2019, Conference or Workshop, How Industry 4.0 and advanced manufacturing could help to reduce procedural caused medical waste? at International Conference on Industry 4.0 and Artificial Intelligence Technologies (INAIT’19), University of Cambridge, Cambridge, UK, 19-22 Aug 2019.

Abstract or Description:

The Industry 4.0 revolution has already started to redefine the way we order, produce and consume ‘things’ today. By implementing advanced manufacturing methods, the medical industry can benefit from a faster and more accurate medical device and material management model to reduce procedure-caused medical redundancies, and reduce the material waste generated. Such change is also reflected in the way medical devices are made and the functionality they deliver. In the current medical industry, doctors and surgeons need to cope with the medical material redundancy issue in their daily routine of diagnosis and treatment. Such redundancies are vital to ensure the safety of patients. However, it also generates a huge amount of waste. Many products and materials become redundant only because they have been exposed to an infectious environment but were never used. The reuse of these products and materials is not preferred due to clinical challenges of safety and sterility.

The use of autonomous robots and artificial intelligence has shown significant reduction of time and human effort required in industries like automobile manufacturing, however, their potential use in the medical industry is yet to be fully developed. This review paper examines the potential of implementing Industry 4.0 and advanced manufacturing methods in reducing the redundancies in medical procedures and hence reduce the amount of waste generated. The key factors identified in this paper will also help laying the groundwork on the existing medical device manufacturing and management model, which aim at a) reducing the inaccuracy of diagnostic data; b) preventing high-risks in the treatment procedures due to limited visualization and simulation support; and c) enhancing the adaptability and customization to specific process requirements.

Official URL: https://easychair.org/publications/preprint/JX94
Subjects: Creative Arts and Design > W200 Design studies
School or Centre: School of Design
Funders: RCA Research Office
Date Deposited: 20 Jun 2019 10:45
Last Modified: 20 Jun 2019 10:45
URI: http://researchonline.rca.ac.uk/id/eprint/3936

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