Medical device-related pressure injury (MDRPI) is a kind of pressure injury that occurs in the course of diagnosis and treatment, and its appearance is similar to that of medical device. Neonatal intensive care unit (NICU) infants are more likely to develop MDRPI than children and adults because of the physiological characteristics of skin and the influence of disease. At present, the occurrence of MDRPI in NICU infants is attracting worldwide attention. Its treatment and nursing consume a large amount of medical resources, which not only affect the outcome of the disease, but also increase the economic burden of the family and society. This article summarizes the MDRPI from three aspects: summary, influencing factors, and evaluation tools. It is expected that NICU nurses will carry out large sample clinical investigation of MDRPI in the future, so as to provide a reference for risk prediction model and risk assessment tools to identify high-risk infants and take effective measures in advance to reduce the incidence of MDRPI.
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.
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.
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.
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.
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.
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.
In recent years, the research on artificial intelligence medical devices has risen markedly along with the expanding application scenarios, exhibiting prominent interdisciplinary characteristics. From 2000 to 2024, the variety of research in artificial intelligence medical devices has significantly increased, while the balance of disciplines has slightly declined, and Simpson's diversity index has continuously increased. Medicine and biology are the main research themes and supportive disciplines in this field. Knowledge from computer science, engineering technology, and mathematics is widely involved and shows an upward trend, while content from the humanities and social sciences is less involved in the research. Compared to the United States and the United Kingdom, China has relatively less biological and chemical knowledge content in the research of this field, but more content related to computer science, engineering technology and material science is involved. This study analyzes the current state and trends of interdisciplinary on artificial intelligence medical devices from the perspective of macro-categories of disciplines, aiming to provide references for research planning, talent training and interdisciplinary cooperation in the field.
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.
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.