Application of Data-Driven Simulation Technology in the Software Verification of Medical Devices

Abstract

This study investigates the application of data-driven simulation technology to enhance medical device software verification systems. Traditional methods, characterized by inefficiency and resource waste, are inadequate for modern demands. We propose a big data-driven simulation model to optimize verification processes, integrating real-case validation and comparative effectiveness analysis. The model addresses vulnerabilities in software verification through structured data analysis and operational improvements. Testing on ICU medical devices (e.g., ECG, X-ray, CT) over six months demonstrated significant efficiency gains: resource allocation improved, verification completion times stabilized, and operational deviations decreased. Key metrics revealed 90% stability in data validation and reduced development risks. Case studies confirmed a 39.25% compliance rate for Class I subsystems using big data techniques. The results highlight the technology ’ s ability to streamline workflows, enhance accuracy, and reduce software vulnerabilities. This approach advances medical device verification by aligning with regulatory requirements and improving clinical safety, offering a scalable framework for future upgrades.

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