Integrating AI with MBSE for Data Extraction from Medical Standards

Model-Based Systems Engineering;Artificial Intelligence;Digitization;Norm Compliance;Large Language Model;Classification

The growing adoption of Model-Based Systems Engineering (MBSE) in the medical sector has prompted a significant emphasis on the digitization of medical standards into norm models aiming to improve data efficiency and establish traceability between norm data from medical standards and other model data, such as SysML models. Despite these efforts, the current digitization activities heavily rely on manual extraction and transformation, particularly from PDF documents into SysML models. Concurrently, the proliferation of Artificial Intelligence (AI) applications in recent years presents an opportunity to enhance these digitization activities. This paper contributes to the integration of AI with MBSE, focusing on automating and optimizing the digitization of medical standards. It explores the initial outcomes of augmenting data extraction from medical standards using advanced AI technologies and integrating them into MBSE practices. The evaluation involves two approaches, an open-source multimodal classifier model and a proprietary large language model. The study assesses these approaches on a medical standard and outlines future work, including the introduction of a third approach.