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

eIF3a is a N ⁶-methyladenosine (m⁶A) reader that regulates mRNA translation by recognizing m⁶A modifications of these mRNAs. It has been suggested that eIF3a may play an important role in regulating translation initiation via m⁶A during infection when canonical cap-dependent initiation is inhibited. However, the death of animal model studies impedes our understanding of the functional significance of eIF3a in immunity and regulation in vivo. In this study, we investigated the in vivo function of eIF3a using eIF3a knockout and knockdown mouse models and found that eIF3a deficiency resulted in splenic tissue structural disruption and multi-organ damage, which contributed to severe sepsis induced by Lipopolysaccharide (LPS). Ectopic eIF3a overexpression in the eIF3a knockdown mice rescued mice from LPS-induced severe sepsis. We further showed that eIF3a maintains a functional and healthy immune system by regulating B cell function and quantity through m⁶A modification of mRNAs. These findings unveil a novel mechanism underlying sepsis, implicating the pivotal role of B cells in this complex disease process regulated by eIF3a. Furthermore, eIF3a may be used to develop a potential strategy for treating sepsis.

Objective:To explore the effect of preoperative visits based on lean management theory in the preoperative psychological state of patients undergoing cardiopulmonary bypass surgery.Methods:According to the coin toss method,72 patients undergoing cardiopulmonary bypass surgery admitted to the Cardiothoracic Surgery Department of Chenzhou First People's Hospital from May 2024 to August 2024 were randomly divided into control group and observation group,with 36 cases in each group.The control group was given routine preoperative visits,and the observation group was given preoperative visits based on lean management theory on the basis of the control group.The psychological state,information demand level,vital signs and satisfaction of the patients were compared between the two groups 1 day before operation and when entering the room.Results:After intervention,the scores of State Anxiety Scale(S-AI)and Amsterdam Preoperative Anxiety and Information Scale(APAIS)were decreased in both groups,and the observation group were significantly lower than control group(P＜0.05).The vital signs(heart rate,systolic blood pressure and diastolic blood pressure)in observation group were lower than control group(P＜0.05).After intervention,the satisfaction of patients in observation group was significantly increased(P＜0.05).Conclusion:The implementation of preoperative visit based on lean management theory can help improve the preoperative psychological state of patients undergoing cardiopulmonary bypass surgery,eliminate anxiety,provide patients with more targeted surgical information support for patients,stabilize patients 'vital signs,and improve patients' satisfaction.

Objective:To assess the professional knowledge proficiency of mainstream large language models (LLMs) in medical laboratory education and to explore their potential as educational aids for medical laboratory technology students.Methods:A comprehensive evaluation was conducted using 400 authentic questions from the 2023 Chinese National Clinical Medical Laboratory Technician Qualification Examination. Five LLMs (Copilot, Grok, Yuanbao, Doubao, and Kimi) were tested through two-round interactions using zero-shot prompting and interaction-optimized prompting strategies. The accuracy of answers and the quality of generated content were evaluated. Performance disparities were analyzed using Cochran's Q test. Content quality was scored through the CLEAR framework (completeness, lack of false information, evidence-based reasoning, appropriateness, relevance).Results:In the first-round test, Doubao achieved the highest overall accuracy rate (375/400). The overall accuracy rates of Doubao and Yuanbao significantly outperformed Copilot and Kimi ( P<0.001). After the second-round interactive optimization, the accuracy rate of Kimi significantly improved ( P<0.05), whereas other LLMs showed slight improvements ( P>0.05). Doubao still had the highest overall accuracy rate (380/400). The overall accuracy rates of Doubao and Yuanbao significantly outperformed Copilot ( P<0.005). Evaluation based on the CLEAR framework revealed that Yuanbao, Doubao, and Kimi significantly outperformed foreign models in the dimensions of evidence-based reasoning ( P<0.003) and completeness ( P<0.05), demonstrating standardized citation of authoritative evidence and superior content quality. Conclusions:The tested LLMs possess extensive medical laboratory knowledge. The accuracy of their answers and the quality of the generated content can be improved through single-question input, specifying evidence requirements, and enabling advanced reasoning functions. Domestic LLMs are comparable to foreign LLMs in terms of accuracy, and have significant advantages in the dimensions of evidence-based reasoning and completeness. LLMs can serve as auxiliary tools for learning professional knowledge in medical laboratory technology.

Objective:To explore the effect of preoperative visits based on lean management theory in the preoperative psychological state of patients undergoing cardiopulmonary bypass surgery.Methods:According to the coin toss method,72 patients undergoing cardiopulmonary bypass surgery admitted to the Cardiothoracic Surgery Department of Chenzhou First People's Hospital from May 2024 to August 2024 were randomly divided into control group and observation group,with 36 cases in each group.The control group was given routine preoperative visits,and the observation group was given preoperative visits based on lean management theory on the basis of the control group.The psychological state,information demand level,vital signs and satisfaction of the patients were compared between the two groups 1 day before operation and when entering the room.Results:After intervention,the scores of State Anxiety Scale(S-AI)and Amsterdam Preoperative Anxiety and Information Scale(APAIS)were decreased in both groups,and the observation group were significantly lower than control group(P＜0.05).The vital signs(heart rate,systolic blood pressure and diastolic blood pressure)in observation group were lower than control group(P＜0.05).After intervention,the satisfaction of patients in observation group was significantly increased(P＜0.05).Conclusion:The implementation of preoperative visit based on lean management theory can help improve the preoperative psychological state of patients undergoing cardiopulmonary bypass surgery,eliminate anxiety,provide patients with more targeted surgical information support for patients,stabilize patients 'vital signs,and improve patients' satisfaction.

Objective:To assess the professional knowledge proficiency of mainstream large language models (LLMs) in medical laboratory education and to explore their potential as educational aids for medical laboratory technology students.Methods:A comprehensive evaluation was conducted using 400 authentic questions from the 2023 Chinese National Clinical Medical Laboratory Technician Qualification Examination. Five LLMs (Copilot, Grok, Yuanbao, Doubao, and Kimi) were tested through two-round interactions using zero-shot prompting and interaction-optimized prompting strategies. The accuracy of answers and the quality of generated content were evaluated. Performance disparities were analyzed using Cochran's Q test. Content quality was scored through the CLEAR framework (completeness, lack of false information, evidence-based reasoning, appropriateness, relevance).Results:In the first-round test, Doubao achieved the highest overall accuracy rate (375/400). The overall accuracy rates of Doubao and Yuanbao significantly outperformed Copilot and Kimi ( P<0.001). After the second-round interactive optimization, the accuracy rate of Kimi significantly improved ( P<0.05), whereas other LLMs showed slight improvements ( P>0.05). Doubao still had the highest overall accuracy rate (380/400). The overall accuracy rates of Doubao and Yuanbao significantly outperformed Copilot ( P<0.005). Evaluation based on the CLEAR framework revealed that Yuanbao, Doubao, and Kimi significantly outperformed foreign models in the dimensions of evidence-based reasoning ( P<0.003) and completeness ( P<0.05), demonstrating standardized citation of authoritative evidence and superior content quality. Conclusions:The tested LLMs possess extensive medical laboratory knowledge. The accuracy of their answers and the quality of the generated content can be improved through single-question input, specifying evidence requirements, and enabling advanced reasoning functions. Domestic LLMs are comparable to foreign LLMs in terms of accuracy, and have significant advantages in the dimensions of evidence-based reasoning and completeness. LLMs can serve as auxiliary tools for learning professional knowledge in medical laboratory technology.

Objective To explore the mechanism by which paeoniflorin improves trabecular meshwork(TM)cell dysfunction.Methods C57BL/6J mice were randomly divided into the following groups:Control group(no treatment),model group(glaucoma model constructed by ocular perfusion)and paeoniflorin group(glaucoma model followed by treatment with 50 mg·kg-1 paeoniflorin).Each group consisted of 12 mice.After 14 days of treatment,intraocular pressure was measured;and trabecular meshwork tissue was collected.TdT mediated dUDP nick end labeling(TUNEL)assay was performed to detect cell apoptosis.Western blot was used to measure protein expression levels.HTMC cells were randomly divided into the following groups:Normal group(cultured under standard conditions),rapamycin group(50 μmol·L-1 rapamycin as an autophagy inducer),experimental-L group(50 μmol·L-1 rapamycin+10 μmol·L-1 paeoniflorin)and experimental-H group(50 μmol·L-1 rapamycin+30μmol·L-1 paeoniflorin).Western blot was used to measure protein expression levels.Methyl thiazolyl tetrazolium(MTT)assay and flow cytometry were applied to evaluate cell proliferation and apoptosis.Results After 14 days of treatment,the intraocular pressure in the control,model and paeoniflorin groups were(12.81±0.83),(26.31±1.85)and(20.64±1.77)mmHg,respectively;the positive cell rate detected by the TUNEL assay were(4.86±0.44)％,(30.32±5.15)％and(15.08±1.92)％,respectively;the levels of microtubule associated protein 1 light chain 3(LC3)Ⅱ/LC3 Ⅰ protein were 0.51±0.06,1.33±0.13 and 0.72±0.08,respectively;Collagen Ⅰ protein levels were 0.45±0.07,1.11±0.12 and 0.72±0.05,respectively.The above indexes of the model group were statistically significant compared with the control group,and the above indexes of the paeoniflorin group were statistically significant compared with the model group(P＜0.001,P＜0.01).The LC3 Ⅱ/LC3 Ⅰ protein levels in the normal,rapamycin,experimental-L,experimental-H groups were 0.40±0.03,1.54±0.10,0.98±0.10 and 0.64±0.06,respectively;the cell viability rates were(100.00±6.25)％,(65.96±6.16)％,(75.22±5.54)％and(82.15±5.14)％,respectively;the apoptosis rates were(4.80±0.37)％,(19.64±0.97)％,(16.10±0.93)％and(13.16±0.94)％,respectively.The above indexes in the rapamycin group were significantly different from those in the normal group,and the above indexes in the experimental-L,-H groups were significantly different from those in the rapamycin group(P＜0.001,P＜0.05).Conclusion Paeoniflorin may improve trabecular meshwork cell dysfunction by regulating autophagy and reducing cell apoptosis,which may provide potential therapeutic strategies for glaucoma.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.