Concurrent engineering and machine learning techniques in medical science

Abstract

In recent years, concurrent engineering (CE) has played an essential role in providing relevant and optimal solutions for multi-disciplinary problems. These are closely associated with various vital tasks, such as product design, manmachine interface for product automation, and achieving the overall performance of the product integrated with cognitive ergonomics. Concurrent Engineering aids in the development of feasible and cost-effective product solutions to ensure the complete satisfaction of the consumer in comparison with their product competitors. The product/ methodology developed with CE helps in achieving (i) enhanced quality, (ii) improved productivity, (iii) optimized design for x-abilities outcomes (like DFM, DFA, and DFX), and (iv) enhanced performance objectives. Concurrent Engineering techniques also help to reduce the gap between the physical and functional arrangement of a successful product. Furthermore, CE-enhanced schemes add to improved efficiency and flexibility. Recently, advents in computerized techniques during the automation of process monitoring and decision-making have been found to be quite useful in a variety of domains. Likewise, the machine-learning (ML) algorithm has supported development of systems with monitoring and decision-making capabilities. Such knowledge-based systems are widely employed in the medical science domain to automate various processes ranging from screening to treatment implementation. When ML schemes are applied in the medical domain, it supports early detection, disease diagnosis, automatic report generation, and treatment planning processes. Such schemes help reduce the diagnostic burden when an extensive number of patients are to be screened. When the ML is associated with CE, the system’s capability, accuracy, and speed automatically increase and the resulting outcome becomes clinically significant. The ML approach helps to examine a considerable number of diseases including, retinal peculiarity, COVID-19, and associated abnormalities. Concurrent Engineering in combination with ML schemes helps to provide better results during patient screening treatment.