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.

The cost of diagnostic errors has been high in the developed world economics according to a number of recent studies and continues to rise. Up till now, a common process of performing image diagnostics for a growing number of conditions has been examination by a single human specialist (i.e., single-channel recognition and classification decision system). Such a system has natural limitations of unmitigated error that can be detected only much later in the treatment cycle, as well as resource intensity and poor ability to scale to the rising demand. At the same time Machine Intelligence (ML, AI) systems, specifically those including deep neural network and large visual domain models have made significant progress in the field of general image recognition, in many instances achieving the level of an average human and in a growing number of cases, a human specialist in the effectiveness of image recognition tasks. The objectives of the AI in Medicine (AIM) program were set to leverage the opportunities and advantages of the rapidly evolving Artificial Intelligence technology to achieve real and measurable gains in public healthcare, in quality, access, public confidence and cost efficiency. The proposal for a collaborative AI-human image diagnostics system falls directly into the scope of this program.

Recently, carbon nanocomposites have garnered a lot of curiosity because of their distinctive characteristics and extensive variety of possible possibilities. Among all of these applications, the development of sensors with electrochemical properties based on carbon nanocomposites for use in biomedicine has shown as an area with potential. These sensors are suitable for an assortment of biomedical applications, such as prescribing medications, disease diagnostics, and biomarker detection. They have many benefits, including outstanding sensitivity, selectivity, and low limitations on detection. This comprehensive review aims to provide an in-depth analysis of the recent advancements in carbon nanocomposites-based electrochemical sensors for biomedical applications. The different types of carbon nanomaterials used in sensor fabrication, their synthesis methods, and the functionalization techniques employed to enhance their sensing properties have been discussed. Furthermore, we enumerate the numerous biological and biomedical uses of electrochemical sensors based on carbon nanocomposites, among them their employment in illness diagnosis, physiological parameter monitoring, and biomolecule detection. The challenges and prospects of these sensors in biomedical applications are also discussed. Overall, this review highlights the tremendous potential of carbon nanomaterial-based electrochemical sensors in revolutionizing biomedical research and clinical diagnostics.