The artificial ventilation system is a multi-factor system with some high uncertain risks which should be under controlled by medical risk management of hospitals. The key suggestions for reducing the accidence caused by ventilator are recommented： 1 ） to clarify the risk factor of ventilators, 2 ） to set up management group of ventilators with a clinical engineer who is good at management and quality control of medical equipment on ventilators, 3 ） to develop ventilator clinical practice for safety use, 4 ） to explore the effective risk monitoring and early warning system and mechanism on ventilator application.
Internet of Things (IoT) technology plays an important role in smart healthcare. This paper discusses IoT solution for emergency medical devices in hospitals. Based on the cloud-edge-device architecture, different medical devices were connected; Streaming data were parsed, distributed, and computed at the edge nodes; Data were stored, analyzed and visualized in the cloud nodes. The IoT system has been working steadily for nearly 20 months since it run in the emergency department in January 2021. Through preliminary analysis with collected data, IoT performance testing and development of early warning model, the feasibility and reliability of the in-hospital emergency medical devices IoT was verified, which can collect data for a long time on a large scale and support the development and deployment of machine learning models. The paper ends with an outlook on medical device data exchange and wireless transmission in the IoT of emergency medical devices, the connection of emergency equipment inside and outside the hospital, and the next step of analyzing IoT data to develop emergency intelligent IoT applications.
Real-world data is been increasingly valued nowadays. This paper combined with related requirements of clinical evaluation of medical devices in China, studied the role of real-world evidence in pre-marketing clinical evaluation of medical devices in terms of technical evaluation, in aim of providing reference for the future application of China's real-world evidence in pre-marketing clinical evaluation.
Objective To introduce the basic information about mad cow disease and the current status of safety control of medical devices derived from mammalian animal tissues. Methods Literature concernedwas reviewed intensively. Results Mad cow disease, also knownas bovine spongiform encephacitis (BSE)， is generally considered from the samesource of Scrapie, and they are caused by the same kind of sponginess brain tissue pathological changes. Mad cow disease is caused by the misfolding of a small protein called Prion. This disease has the character of slowly breaking down the central neuron system of animals. Conclusion Further researches can provide evaluation for safety considerations of medical devices deriving from animal.
Regulatory science of medical devices serves the scientific research and regulatory activities for supervision of medical devices. Principles of science and transparency and conduction of evidence-based study, which is advocated in Evidence-based science(EBS), also apply to regulatory science of medical devices, including using evidence-based scientific tools and methods to demonstrate the safety and effectiveness, as well as quality, efficacy and cost-effectiveness of total life cycle of medical products, target customers, and scope. EBS provides both new methods and tools for regulatory science for medical devices, and provides a new basis for further scientific regulatory decisions.
In 2019, the national government issued the document "Implementation Plan for Supporting the Construction of the Boao Lecheng International Medical Tourism Pilot Area", which allowed the use of innovative drugs and medical devices in medical institution of Boao Lecheng. These medical products had been designed to meet urgent clinical requirements and had been approved by regulatory authorities overseas. Through the use of these medical products, real-world data were generated in the routine clinical practice, based on which real-world evidence might be produced for regulatory decision-making by using scientific and rigorous methods. In March 2020, the first medical device product using domestic real-world data was approved, suggesting that the real-world data initiative in Boao Lecheng achieved initial success. This work also provided important experience for promoting the practice of medical device regulatory decision-making based on real-world evidence in China. Here, we shared the preliminary experiences from the study on the first approved medical device product and discussed the issues on developing a real-world data research framework in Boao Lecheng in attempt to offer insights for future studies.
Real-world data (RWD) in clinical research on specific categories of medical devices can generate sufficient quality evidence which will be used in decision making. This paper discusses the limitations of traditional randomized controlled trials in clinical research of medical devices, summarizes and analyses the applicable conditions of real-world evidence (RWE) for medical devices, interprets the new FDA guidance document on the characteristics of RWD for medical devices, in order to provide evidence for the use of RWE in medical devices in our country.
The intensive care unit (ICU) is a highly equipment-intensive area with a wide variety of medical devices, and the accuracy and timeliness of medical equipment data collection are highly demanded. The integration of the Internet of Things (IoT) into ICU medical devices is of great significance for enhancing the quality of medical care and nursing, as well as for the advancement of digital and intelligent ICUs. This study focuses on the construction of the IOT for ICU medical devices and proposes innovative solutions, including the overall architecture design, devices connection, data collection, data standardization, platform construction and application implementation. The overall architecture was designed according to the perception layer, network layer, platform layer and application layer; three modes of device connection and data acquisition were proposed; data standardization based on Integrating the Healthcare Enterprise-Patient Care Device (IHE-PCD) was proposed. This study was practically verified in the Chinese People’s Liberation Army General Hospital, a total of 122 devices in four ICU wards were connected to the IoT, storing 21.76 billion data items, with a data volume of 12.5 TB, which solved the problem of difficult systematic medical equipment data collection and data integration in ICUs. The remarkable results achieved proved the feasibility and reliability of this study. The research results of this paper provide a solution reference for the construction of hospital ICU IoT, offer more abundant data for medical big data analysis research, which can support the improvement of ICU medical services and promote the development of ICU to digitalization and intelligence.
ObjectiveTo analyze the current situation and international research focuses on the study of medical device risk management. MethodsTo retrieve medical device risk management literature information cited from 2002 to 2011 in PubMed such as high-frequency MeSH; analyze current situation and research focuses of medical device risk management by using bibliometrics, bibliographic item co-occurrence matrix builder (BICOMB), and graphical clustering toolkit (gCluto) for quantitative analysis, high-frequency MeSH term papers cluster visualization analysis. ResultsA total of 7 073 published studies were retrieved, basically suggesting a gradually increasing trend of the number of published papers. The top 3 numbers of first authors' papers referred to three countries: the United States, Britain and Germany, while China ranked twelfth. The top 3 numbers of journal articles referred to the United States, Britain and Holland, while China ranked twenty-second. Twenty journals published more than 50 papers, and all these journals were clinical journals. Thirty-three authors published no less than 5 papers, with the maximum of 18 articles. Totally, there were 124 highfrequency MeSHs. The high-frequency MeSHs were classified into 6 categories by using double cluster analysis: kinds 0 to 4 included risk report, risk analysis, risk assessment and methodology of heart valve prosthesis, coronary stents, peripheral vascular stents, implantable defibrillators and other life support device, surgical repair surgical flaps and minimal invasion surgical device such as laparoscopy; kind 5 focused on safety management, risk control, organization and implementation and other related research based on prevention and control of medical device adverse reaction, medical errors, occupation exposure, and equipment failure. ConclusionThe analysis on international literature on medical device risk management basically shows a gradually increasing trend; most studies published in the clinical medicine journals; research focus on risk assessment, safety management and quality improvement in the application such as angioplasty, artificial prosthesis replacement, plastic surgery, minimally invasive surgery and critical care medicine, and radiology diagnosis and treatment; implantable, life-supported invasive and radiological devices as the main research subject; and characteristics include closely combination between medical device risk management and the application of safe and effective, quality improvement systems for clinical diagnosis and treatment.
The objective of this study is to map the global scientific competitive landscape in the field of artificial intelligence (AI) medical devices using scientific data. A bibliometric analysis was conducted using the Web of Science Core Collection to examine global research trends in AI-based medical devices. As of the end of 2023, a total of 55 147 relevant publications were identified worldwide, with 76.6% published between 2018 and 2024. Research in this field has primarily focused on AI-assisted medical image and physiological signal analysis. At the national level, China (17 991 publications) and the United States (14 032 publications) lead in output. China has shown a rapid increase in publication volume, with its 2023 output exceeding twice that of the U.S.; however, the U.S. maintains a higher average citation per paper (China: 16.29; U.S.: 35.99). At the institutional level, seven Chinese institutions and three U.S. institutions rank among the global top ten in terms of publication volume. At the researcher level, prominent contributors include Acharya U Rajendra, Rueckert Daniel and Tian Jie, who have extensively explored AI-assisted medical imaging. Some researchers have specialized in specific imaging applications, such as Yang Xiaofeng (AI-assisted precision radiotherapy for tumors) and Shen Dinggang (brain imaging analysis). Others, including Gao Xiaorong and Ming Dong, focus on AI-assisted physiological signal analysis. The results confirm the rapid global development of AI in the medical device field, with “AI + imaging” emerging as the most mature direction. China and the U.S. maintain absolute leadership in this area—China slightly leads in publication volume, while the U.S., having started earlier, demonstrates higher research quality. Both countries host a large number of active research teams in this domain.