The rapid development of artificial intelligence technology is driving profound changes in medical practice, particularly in the field of medical device application. Based on data from the U.S. clinical trials registry, this study analyzes the global registration landscape of clinical trials involving artificial intelligence-based medical devices, aiming to provide a reference for their clinical research and application. A total of 2 494 clinical trials related to artificial intelligence medical devices have been registered worldwide, with participation from 66 countries or regions. The United States leads with 908 trials, while for other countries or regions, including China, each has fewer than 300 trials. Germany, the United States, and Belgium serve as central hubs for international collaboration. Among the sponsors, 63.96% are universities or hospitals, 22.36% are enterprises, and the remainder includes individuals, government agencies and others. Of all trials, 79.99% are interventional studies, 94.67% place no restrictions on participant gender, and 69.69% exclude children. The targeted diseases are primarily neurological and mental disorders. This study systematically reveals the global distribution characteristics and research trends of artificial intelligence medical device clinical trials, offering valuable data support and practical insights for advancing international collaboration, resource allocation, and policy development in this field.
Additive manufacturing (AM) is a collection of technologies based on the layer-by-layer manufacturing. Characterized by its direct manufacturing and rapidity, it has been regarded by the Economist Journal as one of the key techniques which will trigger the third industry reformation. The present article, beginning with a brief introduction of the history of AM and the process of its major technologies, focuses on the advantages and disadvantages and medical applications of the technique.
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.
Compared with traditional medical devices, artificial intelligence medical devices face greater challenges in the process of clinical trials due to their related characteristics of artificial intelligence technology. This paper focused on the challenges and risks in each stage of clinical trials on artificial intelligence medical devices for assisted diagnosis, and put forward corresponding coping strategies, with the aim to provide references for the performance of high-quality clinical trials on artificial intelligence medical devices and shorten the research period in China.
Objective To study the USA government’s administrative system about medical device standards as well as the standard making. Methods The relevant documents, regulations, website that USA Food and Drug Administration announced were extensively reviewed, knowing the USA medical device standards synthetically. Results The USA standards system of medical device included regulatory requirements and voluntary consensus standards. This article simply introduced the laws, regulations, performance standards and consensus standards. Conclusion The USA’s administrative system about medical device standards as well as many standards can be referenced.
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.
Active medical device is a kind of medical device which is widely used. In order to realize the goal of high-quality development, product with high reliability is a necessary requirement for the domestic active medical device industry. By means of literature research, data collection, field research, materials comprehensive combing and analysis, this paper systematically analyzes and studies the current situations and the existing problems of reliability and evaluation from the dimensions of Chinese active medical device industry policy, enterprise situation and evaluation method. In addition, by considering the technical characteristics of reliability work, concrete suggestions for solving the problems are given from the directions of standard and guiding principle, so as to provide reference for active medical device industry to develop scientific and objective reliability technical standard system and guiding principle, which are in accord with the current characteristics of Chinese active medical device industry and supervision.
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.