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.
Artificial Intelligence ; Bibliometrics ; Humans ; China ; Equipment and Supplies ; United States ; Biomedical Research

The emergence of new-generation artificial intelligence technology has brought numerous innovations to the healthcare field, including telemedicine and intelligent care. However, the artificial intelligent medical device sector still faces significant challenges, such as data privacy protection and algorithm reliability. This study, based on invention patent analysis, revealed the technological innovation trends in the field of artificial intelligent medical devices from aspects such as patent application time trends, hot topics, regional distribution, and innovation players. The results showed that global invention patent applications had remained active, with technological innovations primarily focused on medical image processing, physiological signal processing, surgical robots, brain-computer interfaces, and intelligent physiological parameter monitoring technologies. The United States and China led the world in the number of invention patent applications. Major international medical device giants, such as Philips, Siemens, General Electric, and Medtronic, were at the forefront of global technological innovation, with significant advantages in patent application volumes and international market presence. Chinese universities and research institutes, such as Zhejiang University, Tianjin University, and the Shenzhen Institute of Advanced Technology, had demonstrated notable technological innovation, with a relatively high number of patent applications. However, their overseas market expansion remained limited. This study provides a comprehensive overview of the technological innovation trends in the artificial intelligent medical device field and offers valuable information support for industry development from an informatics perspective.
Artificial Intelligence ; Patents as Topic ; Humans ; Inventions ; China ; Brain-Computer Interfaces ; Telemedicine ; Equipment and Supplies ; Robotics ; Algorithms

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.
Artificial Intelligence ; Humans ; Clinical Trials as Topic/statistics & numerical data* ; Equipment and Supplies ; Registries ; United States

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.
Artificial Intelligence ; Humans ; Equipment and Supplies ; Interdisciplinary Research

In China, the regulatory framework for medical device procurement and sales, particularly concerning anti-commercial bribery, relies heavily on punitive mechanisms applied after violations occur. Consequently, there is an urgent need to establish a scientific risk regulation framework as a complementary approach. Effective risk-oriented regulatory models require precise identification of risk areas in commercial bribery. Focusing on several major procurement scenarios such as centralized bulk-buying, tendering and bidding processes, in-hospital procurement, and online purchasing, this article analyzes the structural factors contributing to these risks, represented by the absence of certification mechanisms, lack of transparency in information disclosure, and inadequate checks and balances. Based on official risk assessment results, this study applies the theory of power and responsibility to propose a preventive regulatory framework that combines industry self-discipline and administrative oversight. By combining these approaches, the framework aims to develop regulatory measures that can effectively reduce commercial bribery risks and prevent illegal and non-compliant conduct.
China ; Equipment and Supplies/economics* ; Commerce/legislation & jurisprudence* ; Humans ; Risk Assessment

The objective of inspection, testing and supervision of medical devices is to ensure the effectiveness and safety of medical devices in use. Leachables are substances that are leached from medical devices or materials during their clinical use. Leachables are important factors for the safety risks of medical devices. The analysis, detection, and safety evaluation of leachables are important parts of the safety evaluation of medical devices. The allowable limits for leachable substances which are established on the toxicological research provide a scientific basis for the judgment of qualitative and quantitative analysis results. Obtaining more detailed, rigorous and sufficient toxicological research data is of great significance to set highly enforceable product technical indicators. For the establishment of allowable limits for leachable substances, its role in the safety evaluation of medical devices is summarized, and the relevant standards and their implementation status in the testing of medical devices are introduced.
Equipment and Supplies/standards* ; Equipment Safety

Medical equipment management plays a crucial role in enhancing the quality and efficiency of healthcare services. However, traditional management approaches are increasingly inadequate to meet the growing demands of modern healthcare. As intelligent operation and maintenance (O&M) models based on big data, the Internet of Things (IoT), and artificial intelligence (AI) technologies develop, it is imperative to explore their application in medical equipment management. This paper reviews the technical overview of intelligent O&M and discusses the algorithms and challenges of intelligent O&M models based on different technologies. It also proposes issues that need improvement in intelligent O&M models, aiming to provide valuable references for the future development of medical equipment management.
Artificial Intelligence ; Algorithms ; Internet of Things ; Equipment and Supplies ; Big Data

The rapid development of nanomaterials has brought groundbreaking opportunities for high-quality innovation in medical devices, but it has also become a new challenge for regulatory authorities. How to scientifically and rationally evaluate the risks of medical device products with nanomaterials and establish appropriate regulatory classifications have become critical research priorities. To solve this problem, this study focuses on medical devices with nanomaterials, conducts a comparative analysis of domestic and international regulatory classification policies, reviews the current registration status of related products, and provides recommendations on key considerations for the classification and regulation of medical devices with nanomaterials, which aims at promoting high-quality advancement in China's medical device regulation.
Nanostructures/classification* ; Equipment and Supplies/classification*

In recent years, with the development of big data application technology, the real-world data and the corresponding generated real-world evidence have attracted the attention of healthcare regulatory authorities around the world. Regulators recognize that real-world research with specific purposes using real-world data can provide important evidence for regulatory decisions. A total of 90 instances of publicly released on the application of real-world evidence to support regulatory decisions of U. S. Food and Drug Administration are explored, and the positioning and value of real-world evidence in U. S. Food and Drug Administration regulatory decisions are summarized and analyzed, providing references for the use of real-world data and real-world evidence to promote medical devices whole cycle regulation in China.
United States ; United States Food and Drug Administration ; Equipment and Supplies ; Device Approval ; China

Cleaning process validation is an important guarantee for implantable medical devices to ensure product cleanliness. In the manufacturing process, implantable devices usually need to control pollution through cleaning process to achieve a certain cleanliness to ensure the effectiveness of sterilization and product safety. This paper discusses the cleaning process validation of the manufacturing process of the implantable devices based on relevant regulations and technical standards, and proposes the influencing factors and principles to be considered in the cleaning process validation, so as to provide technical reference for the design of the cleaning process validation.
Prostheses and Implants ; Sterilization ; Equipment and Supplies