Advancing model-based systems engineering in biomedical and aerospace research:
A comprehensive review and future directions
DOI:
https://doi.org/10.60087/jklst.v3.n4.p133Keywords:
Systems Engineering, Biomedical, Biosensors, Artificial Intelligence , Machine Learning, Systems Modelling , Model-based Systems Engineering, Dynamic Risk Management FrameworkAbstract
Model-Based Systems Engineering (MBSE) represents a modern methodology for developing complex systems using models, prioritizing alignment with customer preferences through comprehensive systems based modeling. Using PRISMA guidelines, data was gathered from peer-reviewed journals, systematic reviews, case studies, and computational studies from databases such as PubMed and Google Scholar, from the past 24 years. The study provides a comprehensive view of the current state of MBSE applications in healthcare and engineering addressing the practical challenges they face, offering strategic suggestions to improve future outcomes. This research introduces the Dynamic Risk Management Framework (DRMF), designed to leverage real-time data and predictive analytics to bolster system reliability and performance. The reviewed articles illuminate the essential role of MBSE in creating sophisticated systems and emphasize the need for improved modeling language integration, standardized processes, and increased interoperability. Further studies are required to validate its effectiveness and overcome its current limitations. As an emergent discipline within systems engineering, MBSE holds significant promise for future development, positioning itself as a critical tool for optimizing diverse fields of application. Further investigations are essential to validate MBSE's effectiveness and address its existing limitations.
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