Research on Precision Detection and Intelligent Early Warning of Medical Devices: Current Status, Practical Challenges, and Development Pathways

Abstract

With the increasing intelligence, connectivity, and complexity of medical devices, conventional periodic inspection and reactive maintenance methods can no longer fully meet the requirements of clinical safety and lifecycle risk management. Precision detection and intelligent early warning integrate multisource sensing, condition monitoring, fault diagnosis, artificial intelligence, and risk prediction to identify device abnormalities and potential safety hazards at an early stage. Using literature review, comparative analysis, and inductive synthesis, this paper examines the core concepts, technical logic, current applications, practical challenges, and development pathways of precision detection and intelligent early warning for medical devices. It reviews representative applications in manufacturing quality inspection, clinical operation monitoring, fault diagnosis, predictive maintenance, and post-market surveillance, and compares rule-based methods, machine learning, deep learning, data fusion, and digital-twin technologies. The analysis shows that intelligent technologies can improve fault-identification accuracy, detect complex degradation patterns, and support proactive risk intervention. However, their practical application remains constrained by insufficient high-quality fault data, inconsistent data standards, limited model generalizability and interpretability, false alarms, inadequate system interoperability, cybersecurity risks, and incomplete regulatory frameworks. Accordingly, this paper proposes establishing standardized medical device data systems, developing trustworthy and generalizable artificial intelligence models, constructing lifecycle-oriented monitoring mechanisms, optimizing risk-graded warning and human–AI collaboration, and improving technical standards and regulatory systems. These pathways may facilitate the transition of medical device safety management from periodic inspection and corrective maintenance toward continuous monitoring, predictive maintenance, and proactive risk governance.