The development and application of medical artificial intelligence (AI) large models in the medical field have brought opportunities and challenges to regulation. By reviewing the application and technical characteristics of AI large models in the medical field, starting from the perspective of medical device classification management, drawing on foreign regulatory experience, and based on China's regulatory reality, the technical characteristics of medical AI large models are analyzed. Provide suggestions for defining attributes and categorizing AI large models, and offer reference for their technical classification management.
To address the current challenges of difficult removal and potential residuals of soft tissue foreign bodies, this paper utilizes the principle of different light reflection degrees between foreign bodies and tissues to construct a foreign body localization device based on modified PMMA optical fibers. An optical fiber light transmission method is designed for the localization of soft tissue foreign bodies. To verify the practicality and reliability of the device, tests and comparisons are conducted on biological tissue models. The results show that the device can locate foreign bodies more efficiently and at lower cost, facilitating the complete removal of soft tissue foreign bodies.
As a Class Ⅱ medical device, clinical mass spectrometers face challenges in performance stability when operating in long-term, high-volume, and high-matrix clinical application scenarios. However, a unified standard method for evaluating performance stability has not yet been established domestically or internationally. This study aims to construct a scientific and universal evaluation system for the key performance stability of clinical mass spectrometers based on the statistical process control (SPC) theory, providing technical support for the access review of regulatory authorities and the quality improvement of manufacturing enterprises. Firstly, based on the division of clinical application needs and risk levels, key performance parameters such as detection limit and anti-interference capability, as well as their importance levels, are determined. Secondly, key performance data are collected by simulating actual clinical operating scenarios, and the X-bar-R control chart is used to judge the statistical steady state. Subsequently, the stability capability index (SCI) is developed with the process performance index (PP) as the core for technical steady-state evaluation. Finally, the stability levels are classified through the combined determination of statistical steady state and technical steady state. The effectiveness of the method is verified through the detection limit test using ICP-MS. The results show that this method can not only effectively evaluate the key performance stability of the instrument but also identify the root causes of instrument performance fluctuations. The conclusion indicates that the SPC-based method for evaluating the key performance stability of clinical mass spectrometers is scientific, operable, and economical. It can not only accurately predict the performance degradation trend of instruments and warn of failure risks but also provide a unified standard for the whole-life cycle quality control of medical devices. It is applicable to the stability evaluation of different types of clinical mass spectrometers and is of great significance for standardizing industry quality standards and improving clinical application safety.
Objective:To construct a failure mode and effects analysis (FMEA) model for risk management of cone- beam computed tomography (CBCT) equipment and improve the level of equipment management. Methods:The lean management tool FMEA was used for full-process management evaluation. A total of 15 CBCT devices were selected, and four types of failure modes, including technical performance, environmental interference, system software, and operation norms, were identified. A total of 58 failure modes were confirmed, of which 10 were classified as high-risk effective interventions were carried out on the high-risk items, and a questionnaire survey was conducted to evaluate the effectiveness of the interventions. Results:The χ 2 test and t-test were performed using SPSS 29.0. After the intervention, the mean risk priority number (RPN) for all four failure mode categories decreased significantly. The differences in both RPN values and work quality scores before and after the intervention were statistically significant. Conclusion:The FMEA model can be used for refined and scientific risk management of cone-beam CT equipment, effectively enhancing the operational capacity of medical equipment and improving the quality of clinical services.
In the process of ultrasound imaging, optimizing the transmit sequence strategy, adaptive beamforming algorithms, and image post-processing can significantly improve spatial resolution and tissue contrast. However, the direct integration of these advanced techniques invariably escalates the consumption of computational resources, causing a significant performance degradation in real-time imaging. This creates a critical trade-off challenge between real-time performance and image quality in ultrasonic diagnosis. In order to solve this problem, in this study, we introduce an imaging method that selectively enhances regional image quality while preserving real-time capability. This method can comprehensively consider the needs of different imaging scenarios and design one or more signal processing links specifically in local areas, thereby achieving intelligent characteristic enhancement functions such as noise suppression and texture enhancement. The results of the phantom test show that the noise suppression function of HD Scope+ has improved by an average of 59.05%, 0.63 dB, and 0.0207 in contrast ratio (CR), contrast-to-noise ratio (CNR), and generalized contrast-to-noise ratio (gCNR) indexes compared to those of conventional B-mode images. In addition, the texture enhancement function of the HD Scope+ has increased the fullwidth at half-maximum (FWHM) index by a maximum of 77.12%. The diagnostic results of hemangioma and pancreatic cyst indicate that the HD Scope+ can provide richer local image characteristics, which is helpful for improving the accuracy of ultrasound diagnosis.
Against the backdrop of the deepening Healthy China strategy, the integrated innovation and systematic translation of medical devices have become pivotal pathways for enhancing healthcare service capabilities and overcoming critical technological bottlenecks. Addressing current challenges such as fragmented resources, inefficient collaboration, and translational barriers in medical device innovation, this paper establishes an integrated innovation mechanism centered on the deep convergence of "medicine-research-industry-academia-application", and designs a full-cycle systematic translation model spanning "needs identification - R&D validation - regulatory approval - market application". Through strategies including multi-stakeholder collaborative platform development, big-data and AI empowerment, and optimization of policy support systems, efficient allocation of innovation resources and dynamic optimization of translation processes are achieved. Case studies of United Imaging Healthcare and Mindray Medical confirm that integrated innovation significantly shortens R&D cycles and improves translation efficiency, offering a systematic solution to accelerate the domestication of high-end medical devices and strengthen global competitiveness.
ObjectiveWith the global obesity crisis worsening and China launching the "Weight Management Year" initiative, demand for weight management medical devices has grown significantly. This study focuses on two key issues: the lack of a unified global classification framework for such devices and the fragmented understanding of international regulatory differences. MethodsFrom the technical perspective of medical device classification and determination, this paper reviews the regulatory landscape of such devices in the United States, Europe, Japan, South Korea, Australia, and Canada. Focusing on intragastric balloons, cryolipolysis devices, and medical-grade smart body fat scales, it compares classification approaches, restrictions on claims, and clinical validation requirements, while also analyzing controversies such as vague definitions and inconsistent risk assessments. ResultsThe analysis shows notable differences in regulatory philosophies across countries. The United States uses a model centered on risk and efficacy; the European Union emphasizes technical substantiation and full product lifecycle management; Japan and South Korea prioritize adaptability to local populations. A critical problem is the lack of unified core performance parameters: for example, the required fat reduction rate for cryolipolysis devices is ≥20% in the US but ≥15% in the EU, and there are no consistent thresholds for body fat measurement accuracy. This not only creates regulatory ambiguity but also leaves room for regulatory arbitrage. Overall, the global regulatory environment for weight management devices remains uncoordinated, facing challenges from vague definitions and inconsistent standards. ConclusionDrawing on international experiences and China’s specific regulatory context, this study proposes a framework for a China-specific regulatory system for weight management devices. Core recommendations include: refining technical and clinical standards for key products like medical-grade smart body fat scales; strengthening management of intended use claims to clarify regulatory boundaries; and establishing a closed-loop, full-lifecycle management system integrated with real-world data. These measures are expected to shift China’s regulatory model from reactive response to proactive leadership, ultimately contributing to a "Chinese solution" to both high-quality industrial development and global regulatory harmonization.
Photoacoustic-ultrasound dual-modal imaging represents an emerging hybrid imaging technique that combines the functional advantages of photoacoustic imaging with the high-resolution structural characterization capabilities of ultrasound imaging. This article comprehensively summarizes the research status and progress from both system-level and algorithmic perspectives. In hardware development, breakthroughs in novel ultrasound transducers and multimodal probe architecture designs have significantly enhanced tissue penetration depth and signal acquisition efficiency while reducing system complexity. In terms of imaging algorithms, the synergistic innovation of physics-model-driven and data-driven approaches has achieved precise quantitative reconstruction of optical absorption parameters and sound velocity. In particular, deep learning frameworks employing multimodal feature fusion and adaptive optimization strategies have effectively addressed the limitations of conventional methods in noise suppression and computational efficiency. This article not only provides a reference for theoretical research and clinical applications of multi-modal imaging technology but also explores its broad prospects in the development of intelligent diagnostic / therapeutic devices and precision medicine practices.
To address the need for metabolic disorder assessment, an evaluation model of metabolic flexibility was developed based on the dynamic characteristics of the respiratory quotient (RQ). This study enrolled 56 participants from the First Affiliated Hospital of University of Science and Technology of China, including 20 participants in the metabolically flexible group and 36 participants in the metabolically inflexible group. Clinical data were collected. Indirect calorimetry was used to measure RQ values at five time points during an oral glucose tolerance test. Features derived from RQ were extracted. A staged feature selection process was conducted to identify the most relevant predictors. The synthetic minority over-sampling technique (SMOTE) was applied to alleviate class imbalance. Evaluation models were established using logistic regression (LR), K-nearest neighbors (KNN), XGBoost, and random forest (RF) algorithms. All models demonstrated strong performance, with comprehensive scores exceeding 0.920. The RF model achieved the best performance, with an accuracy of 0.941, an F1-score of 0.957, an AUC of 0.985, and a comprehensive score of 0.961. The proposed method combines high predictive accuracy with physiological interpretability, providing a reliable basis for early screening of metabolic disease risk.
Blockchain technology, with its characteristics of decentralization, immutability, traceability, and high transparency, offers innovative solutions for the innovative development of the medical engineering field. As a key support for ensuring the quality, safety and efficiency of medical care, the medical engineering field is currently facing multiple challenges such as complex medical device management, severe data silos, opaque supply chains and prominent information security risks. This paper systematically summarizes the research status and development trends of blockchain in the medical engineering field. Firstly, it elaborates on the basic principles of blockchain technology and its high compatibility with the demands of medical engineering. Secondly, it focuses on three core dimensions: the full life cycle management of medical device, the security and sharing of medical device data, and the management of medical device and consumables supply chains, to deeply analyze the main application scenarios, key technologies, and existing research progress of blockchain. Thirdly, this paper discusses the typical architecture of medical engineering application systems based on blockchain and integrates and analyzes the empirical data on key performance indicators in existing research. Fourthly, this paper systematically summarizes the multi-dimensional challenges faced by the application of blockchain technology in the medical engineering field, including technical, regulatory, cost and organizational aspects, and conducts a comprehensive assessment of its feasibility. Finally, it looks forward to the future research directions in this field.
To examine how post-market vigilance can capture injury signals linked to prolonged use, individual adverse-event reports on active medical devices held by Shanghai Center for Adverse Drug and Medical Device Reaction Monitoring were screened. Reports explicitly mentioning "prolonged/expired use" were analyzed descriptively and key cases were dissected. Results show that 59.24% of all prolonged-use reports involved ten device types, led by electronic endoscopes, ECG equipment and dialysis machines. Common characteristics include complex architecture, non-replaceable or high-cost key components, and high software dependency. Case studies indicate that the use of high-frequency electrosurgical leads, high-pressure injection tubing, and electric suction units beyond their validated life-span directly precipitates events such as lead arcing, tubing burst, and suction failure, and is accompanied by systemic deficiencies of "obsolete labeling and absent in-hospital maintenance strategies". A post-market dynamic service-life assessment mechanism is proposed that integrates component-level life-cycle management, residual-risk communication, and timely instruction updating into a closed-loop governance framework.
This study presented the design, and preliminary validation of a dual-mode focused ultrasound system for thermal ablation and histotripsy based on a single transducer. The acoustic characteristic measurements indicated that the maximum acoustic power was approximately 185 W in thermal ablation mode and exceeded 6 000 W in histotripsy mode. Under different treatment parameters, the ex vitro experimental results demonstrated that the system could achieve coagulative necrosis through thermal ablation and liquefaction through histotripsy, which disintegrates tissue into homogenate, respectively. Therefore, this system can be used for preclinical research on the thermal and mechanical effects of focused ultrasound.
To address the insufficient human-machine interaction adaptability in current lower-limb rehabilitation exoskeletons, this study proposes a lower-limb assistive exoskeleton featuring active actuation at the hip and knee joints and passive following at the ankle joint. A rigid self-adaptive human-machine interface is adopted to provide both effective assistive torque and structural compliance, aiming to deliver gait assistance for individuals with muscle weakness while improving wearing comfort. First, a bionic geometric structure of the exoskeleton was constructed based on human lower-limb biomechanics, and passive degrees of freedom in the attachment components were designed using a serial-chain topology. The forward kinematics model of the exoskeleton was then established and verified using the D-H method. Finally, a human-exoskeleton coupled model was built on the OpenSim platform, in which the human model was configured to three muscle strength conditions (100%, 80% and 60% of the maximal isometric force). The assistive performance and interaction characteristics of the designed exoskeleton were evaluated through changes in two key indicators-overall metabolic consumption and human-robot interaction forces-as well as variations in hip and knee flexor-extensor muscle forces. The results show that the proposed exoskeleton effectively reduces human-machine interaction forces, with peak interaction forces at the thigh decreasing from 70 N to 20 N and those at the shank decreasing from 150 N to 30 N. The overall metabolic cost of the wearer was reduced by 13.8%-15.4%, and the muscle force outputs of major hip and knee muscle groups markedly decreased. These findings validate the rationality of the design and demonstrate its performance in adaptive human-machine interaction assistance, highlighting its application potential in rehabilitation training and gait assistance for individuals with muscle weakness.
This paper conducts a systematic study focusing on the key role of density resolution in medical imaging. Density resolution stands as a critical performance metric in medical image quality assessment, directly determining an imaging system's capability to discern subtle density differences between tissues. It fundamentally reflects the capacity of imaging equipment to differentiate adjacent anatomical structures with similar signal characteristics. Enhancing density resolution significantly improves sensitivity in detecting early pathological changes, thereby providing more reliable imaging evidence for clinical decision-making. With the growing demands of precision medicine and early disease screening, improving density resolution while maintaining imaging efficacy and safety has emerged as a pressing challenge in medical imaging technology development. This study conducts a comprehensive analysis from multiple perspectives: the principles and threshold analysis of density resolution, influencing factors and performance enhancement strategies, along with technical safeguards and future challenges. The research systematically elaborates on technical optimization pathways for precision improvement in this field.
Objective:To address the prevalent issue of resource waste caused by the "fixed-term scrapping" policy in medical device management, this study aims to establish a scientific residual value evaluation system based on real-world data (RWD). The goal is to provide theoretical and empirical evidence for optimizing medical resource allocation and breaking the traditional rigid age-based mandatory retirement model. Methods:Defibrillators, a typical category of emergency equipment in a large tertiary Grade A hospital, were selected as the pilot sample. A technical evaluation model incorporating the Process Capability Index ( C p k ) and a health economic evaluation model based on the replacement cost method were constructed. A retrospective analysis was conducted on 191 valid quality control records from 85 in-use devices to quantitatively evaluate the actual performance status and potential economic value of devices serving beyond their recommended lifespan. Results:Empirical analysis demonstrated no significant linear correlation between the service life of defibrillators and their core performance. Under standardized maintenance, 37.6% of the devices that had served for over 12 years still exhibited excellent process capability ( C p k > 1 . 33 ). Implementing a condition-based service life management strategy for this specific category of equipment alone could theoretically save the hospital approximately ¥ 1 , 152 , 000 in replacement costs. Conclusion:The current fixed-term scrapping system for medical equipment poses a significant risk of resource misallocation. This study validates the effectiveness of a dual-dimensional (technical and economic) evaluation model in identifying the residual value of equipment. It is suggested that medical institutions implement classified management based on device characteristics-establishing a hierarchical life-extension management catalog based on the C p k index-to achieve refined fixed assets management and significant cost efficiency while ensuring medical safety.
Acupuncture, a traditional Chinese medical therapy, is undergoing a progressive intelligent transformation driven by modern technology. The integration of contemporary technology with traditional acupuncture has resulted in the evolution of smart acupuncture equipment. This evolution has transformed acupuncture from a diagnostic and therapeutic tool into an intelligent system encompassing a wide range of applications, including precision diagnosis, treatment and health management. This paper presents a systematic review of core technological advancements in smart acupuncture equipment, including perception and positioning, decision-making and intelligent algorithms, and execution and control. Furthermore, it delineates the contemporary state of research and development for innovative equipment such as smart acupuncture robots and intelligent acupuncture treatment units. Concurrently, it analyzes challenges constraining development, such as multidisciplinary talent shortages, incomplete industry standards, low research-to-practice conversion rates, and data security concerns. A number of strategies are proposed for advancing further development, including medical-engineering integration, standardisation initiatives, translational medical research, and data governance.
Particle Arc Therapy (PAT) represents a cutting-edge innovation in modern radiation therapy, encompassing two principal modalities: Spot-Scanning Proton Arc Therapy (SPArc) and Spot-Scanning Heavy Ion Arc Therapy (SHArc). By delivering beams continuously along an arc trajectory, PAT enables highly conformal dose distributions and improved sparing of organs at risk (OAR), particularly in complex anatomical sites. Existing studies have demonstrated that, compared with conventional Intensity-Modulated Proton Therapy (IMPT), SPArc can reduce the mean OAR dose by approximately 10%-20% (P<0.05), while achieving enhanced plan robustness and delivery efficiency. SHArc, benefiting from the high Linear Energy Transfer (LET) characteristics of heavy ions, is theoretically advantageous for the treatment of radioresistant tumors. This review provides a comprehensive overview of the physical and technical foundations of PAT, its planning optimization strategies, and recent clinical and preclinical developments. Challenges related to robustness verification, motion management, and cost-effectiveness are also discussed. Overall, PAT—particularly SPArc—exhibits promising translational potential and is poised to become an important direction in the future of precision radiotherapy.
ObjectiveThis article describes the design and fabrication of individualized pelvic prostheses by integrating three-dimensional (3D) printing technology with design software, and evaluates their feasibility and clinical efficacy. MethodsBased on the patient’s anatomical structure and biomechanical requirements, individualized pelvic prostheses were designed using digital reconstruction software, manufactured by combining 3D printing and machining technologies, and finally verified for feasibility through clinical application. ResultsThrough adequate doctor-engineer interaction and detailed preoperative planning, precise individualized resection of the patient’s tumor range was completed; guided by the pelvic tumor classification theory, individualized pelvic prostheses were designed and manufactured. Clinical application showed that the prostheses were consistent with the surgical plan and accurately implanted, with complete postoperative pelvic reconstruction and good healing. ConclusionThis article summarizes the previous theoretical basis and sorts out the key design points of prostheses corresponding to different pelvic defect classifications; by using digital reconstruction software, combined with 3D printing technology and machining processes, the design and fabrication of individualized pelvic prostheses were completed; the feasibility and effect of the prosthesis design were verified through clinical application, providing a new approach for clinical medicine to repair defects after pelvic tumor osteotomy and other special lesions.
ObjectiveAn improved failure mode and effects analysis (FMEA) model is constructed to enhance the accuracy of reliability assessment for wide-detector computed tomography equipment. MethodsA multidisciplinary expert panel is established. A scoring system is used to quantitatively evaluate various potential failure modes, and expert weights are introduced for weighted calculation to achieve objective ranking of risk priority. ResultsThe overheating of the X-ray tube and system communication interruption are identified as the highest-risk failure modes. Significant differences in equipment reliability under different clinical usage patterns are quantitatively revealed. ConclusionThe improved FMEA method can effectively identify key failure links, providing a reliable basis for formulating differentiated and precise preventive maintenance strategies, which is helpful for ensuring the continuous and stable operation of equipment.
Saliva, as a biological sample capable of reflecting the physiological and pathological status of the body, holds significant application value in disease screening and monitoring due to its advantages such as ease of collection, safety, and non-invasiveness. To promote its efficient use in clinical practice, the development of point-of-care testing (POCT) technologies for saliva is key to providing rapid, accurate, and cost-effective diagnostic solutions. In light of this, this article systematically reviews the latest advances in POCT technologies for salivary biomarkers, covering a variety of methods including dry chemistry, immunochromatography, chemiluminescence, electrochemistry, quartz crystal microbalance (QCM), surface plasmon resonance (SPR), microfluidics, biochips, molecular diagnostics, and artificial intelligence-assisted diagnostics. It focuses on representative technologies and products that are either commercially available or have translational potential, delving into their technical principles, design concepts, performance metrics, and application scenarios. Finally, the article summarizes the key challenges and future opportunities facing saliva POCT technologies in the process of clinical translation, aiming to provide academic support for their further research advancement and industrial development.
