All sectors have an increasing interest in smart phone applications based on their many advantages that support business, especially the medical sector, which is constantly competing to develop the medical services provided, and accordingly in this research study we industrialized a mobile medical supplies and equipment ordering application (mobile medical app) classic and make an effort to authenticate it factually. When clients (hospitals doctors) create consumptions on the application, three dimensions can be identified: platform emotion stage, fear effect, and familiarity with product. This research designed to reinforce and brighten the most important magnitudes that improve a physician’s judgment of mobile medical app and the purpose to usage. Furthermore, this study inspected the availability of the model between hospital physicians in UAE. The classic ideal was observed by means of a model of 340 UAE clinic physicians and their personal assistant who utilize mobiles facilities in overall. The review technique, a calculable method, was applied; the fractional smallest cubes organizational calculation exhibiting systems was owned to inspect the planned agenda. The platform emotion dimension, especially fear and resistance to change, and the familiarity with the products were evaluated, and it was discovered that these factors positively influenced the objective to use the application. And the other side, the first dimension of emotion, fear, manifested as “apparent threat”, had no outcome on the purpose to using. These discoveries recommended that scholars should emphasis more on the facilities, merchandises, and the key task of the mobile medical app to control their inspirations on clients’ ordering purpose. This will progress the purchasing ways associated to acquiring medicinal materials utilizing mobile medical app and/or on other operational stages in unambiguously in UAE and the Central East at great.

Resisting the adoption of medical artificial intelligence (AI), it is suggested that this opposition can be overcome by combining AI awareness, AI risks, and responsibility displacement. Through effective integration of public AI dangers and displacement of responsibility, some of these major concerns can be alleviated. The United Kingdom’s National Health Service has adopted the use of chatbots to provide medical advice, whereas heart disease diagnoses can be made by IBM’s Watson. This has the ability to improve healthcare by increasing accuracy, efficiency, and patient outcomes. The resistance may be due to concerns about losing jobs, anxieties about misdiagnosis or medical mistakes, and the consciousness of AI systems drifting more responsibility away from medical professionals. There is hesitancy among healthcare professionals and the general public about the deployment of AI, despite the fact that healthcare is being revolutionised by AI, its uses are pervasive. Participants’ awareness of AI in healthcare, AI risk, resistance to AI, responsibility displacement and ethical considerations were gathered through questionnaires. Descriptive statistics, chi-square tests and correlation analyses were used to establish the relationship between resistance and medical AI. The study’s objective seeks to collect data on primary and public AI awareness, perceptions of risk and feelings of displacement that the professionals have regarding medical AI. Some of these concerns can be resolved when AI awareness is effectively integrated and patients, healthcare providers, as well as the general public are well informed about AI’s potential advantages. Trust is built when, AI related issues such as bias, transparency, and data privacy are critically addressed. Another objective is to develop a seamless integration of risk management, communication and awareness of AI. Lastly to assess how this comprehensive approach has affected hospital settings’ ambitions to use medical AI. Fusing AI awareness, risk management, and effective communication can be used as a comprehensive strategy to address and promote the application of medical AI in hospital settings. An argument made by Chen et al. is that providing training in AI can improve adoption intentions while lowering complexity through the awareness of AI.

This study examined the dissatisfaction among Chinese medical students with online medical English courses, which overemphasize grammar yet fail to provide practical opportunities related to medical situations. This study compared co-teaching’s effects, involving native and non-native instructors, with a single-instructor (traditional) model on student satisfaction in online medical English courses. Using a qualitative design, pre- and post-course interviews were conducted with 49 second-year medical students across seven classes, exploring their perceptions of instruction, curriculum, and course satisfaction. The findings indicated that the co-teaching model improved student engagement and satisfaction, not specifically due to the native English-speaking instructor but likely because of the focus on more interactive and discussion-oriented strategies. In contrast, the single-instructor model maintained the traditional grammar-focused instruction, leading to lower satisfaction levels. Both instructional models faced limitations related to their reliance on textbooks for delivering core material needed for the course’s comprehensive exam. These results suggest that the instruction design and approach, rather than the native instructor alone, was the main driver of positive outcomes in co-teaching. The study’s findings suggest a need for curriculum reforms that reduce textbook dependence and incorporate more practical, interactive learning strategies. Future research should consider applying various research techniques, such as mixed-method approaches, longitudinal studies, and experimental designs, to comprehensively assess the long-term effects of instructional strategies and curriculum innovations on student outcomes.

Background: Despite China’s 1.4 billion population and massive investment in improving medical education, there is no transformational national or international course focused on emergency trauma care. In order to overcome recognized deficiencies, we developed an affordable knowledge and skills workshop called Essential Trauma Critical Care China (ETCCC). Methods: Pre-course and post-course MCQs were used to test knowledge and simulation scenarios quantified clinical competence. Structured feedback was obtained. To evaluate the effect of ETCCC on staff performance, we analyzed the clinical records and questioned resuscitation team peers for trauma patients requiring resuscitation room intervention in the 10 consecutive patients before and after the workshops. Results: During 2022–2023, five workshops were delivered to participants from six hospitals in two Chinese provinces. Cost per participant did not exceed US$125. Fifty-eight doctors and 37 nurses participated. For all delegates pre-course knowledge scores increased from mean 35% to 70% post-course. 99% (n = 82/83) participants reached the required standard in the post-course written test. Post-course skills tests scores were mean 67% for doctors and 84% for nurses. Nurses demonstrated significant improvements in the rate and quality of trauma history acquisition as well as triage skills after the course (all p < 0.01). Doctors scored significant improvement in the areas of leadership and teamwork, care of cervical spine, circulation assessment and fluid resuscitation (all p < 0.02). Conclusion: Essential Trauma Critical Care China (ETCCC) is the first economically developed medical educational tool shown to improve performance of emergency room staff. Its success may have relevance for trauma-care education in similar medium-resource environments.

Background: Traditional education in neurosurgery primarily relies on observation, giving residents and interns limited opportunities for clinical practice. However, the development of 3D printing has the potential to improve this situation. Based on bibliometrics, we analyze the application of 3D printing technology in neurosurgery medical education and surgical training. Methods: We searched the publications in this field in Web of Science core collection database from September 2000 to September 2023. VOS viewer, Citespace and Microsoft Office Excel were used to visually analyze and draw knowledge graphs. Results: A total of 231 articles and reviews were included. The United States is the country with the largest volume of articles and Mayo Clinic is the leading organization in this field. Partnership between countries, authors and institutions is also presented. World Neurosurgery is the journal with the highest number of publications. The top three key words by occurrence rate are “3D printing”, “surgery” and “simulation”. Conclusions: In recent years, more and more attention has been paid to the research in this field. According to bibliometric analysis, “accuracy” and “surgery simulation” are the research focuses in this field, while “augment reality” is the potential research target.