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Given the widespread application of ChatGPT, we aim to evaluate its proficiency in the emergency medicine specialty written examination. Additionally, we compare the performance of GPT-3.5, GPT-4, GPTs, and GPT-4o. The research seeks to ascertain whether custom GPTs possess the essential capabilities and access to knowledge bases necessary for providing accurate information, and to explore the effectiveness and potential of personalized knowledge bases in supporting the education of medical residents. We evaluated the performance of ChatGPT-3.5, GPT-4, custom GPTs, and GPT-4o on the Emergency Medicine Specialist Examination in Taiwan. Two hundred single-choice exam questions were provided to these AI models, and their responses were recorded. Correct rates were compared among the four models, and the McNemar test was applied to paired model data to determine if there were significant changes in performance. Out of 200 questions, GPT-3.5, GPT-4, custom GPTs, and GPT-4o correctly answered 77, 105, 119, and 138 questions, respectively. GPT-4o demonstrated the highest performance, significantly better than GPT-4, which, in turn, outperformed GPT-3.5, while custom GPTs exhibited superior performance compared to GPT-4 but inferior performance compared to GPT-4o, with all p < 0.05. In the emergency medicine specialty written exam, our findings highlight the value and potential of large language models (LLMs), and highlight their strengths and limitations, especially in question types and image-inclusion capabilities. Not only do GPT-4o and custom GPTs facilitate exam preparation, but they also elevate the evidence level in responses and source accuracy, demonstrating significant potential to transform educational frameworks and clinical practices in medicine.

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BACKGROUND: Blockchain technology has revolutionized the healthcare sector, including emergency medicine, by integrating AI, machine learning, and big data, thereby transforming traditional healthcare practices. The increasing utilization and accumulation of personal health data also raises concerns about security and privacy, particularly within emergency medical settings. METHOD: Our review focused on articles published in databases such as Web of Science, PubMed, and Medline, discussing the revolutionary impact of blockchain technology within the context of the patient journey through the ED. RESULTS: A total of 33 publications met our inclusion criteria. The findings emphasize that blockchain technology primarily finds its applications in data sharing and documentation. The pre-hospital and post-discharge applications stand out as distinctive features compared to other disciplines. Among various platforms, Ethereum and Hyperledger Fabric emerge as the most frequently utilized options, while Proof of Work (PoW) and Proof of Authority (PoA) stand out as the most commonly employed consensus algorithms in this emergency care domain. The ED journey map and two scenarios are presented, exemplifying the most distinctive applications of emergency medicine, and illustrating the potential of blockchain. Challenges such as interoperability, scalability, security, access control, and cost could potentially arise in emergency medical contexts, depending on the specific scenarios. CONCLUSION: Our study examines the ongoing research on blockchain technology, highlighting its current influence and potential future advancements in optimizing emergency medical services. This approach empowers frontline medical professionals to validate their practices and recognize the transformative potential of blockchain in emergency medical care, ultimately benefiting both patients and healthcare providers.

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Telemedicine is the use of technology to deliver healthcare services from a distance. In some countries, telemedicine became popular during the COVID-19 pandemic. Its increasing popularity provides new research opportunities to unveil users' perceptions toward its adoption and continued use. Existing studies have provided limited information and understanding of Taiwanese users and the various sociodemographic factors that influence their intention to use telemedicine services. Thus, the goals of this study were twofold: identifying the dimensions of perceived risks of telemedicine services in Taiwan and providing specific responses to those perceptions as well as determining strategies to promote telemedicine to local policymakers and influencers by providing a better understanding of the perceived risks in relation to socioeconomic status. We collected 1000 valid responses using an online survey and found performance risk to be the main barrier, which was followed by psychological, physical, and technology risks. Older adults with lower levels of education are less likely to use telemedicine services compared to other categories because of multiple perceived risks, including social and psychological concerns. Understanding the differences in perceived risks of telemedicine services by socioeconomic status may aid in identifying the actions required to overcome barriers and may consequently improve adoption of the technology and user satisfaction.

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BACKGROUND: Interest in the metaverse has been growing worldwide as the virtual environment provides opportunities for highly immersive and interactive experiences. Metaverse has gradually gained acceptance in the medical field with the advancement of technologies such as big data, the Internet of Things, and 5 G mobile networks. The demand for and development of metaverse are different in diverse subspecialties owing to patients with varying degrees of clinical disease. Hence, we aim to explore the application of metaverse in acute medicine by reviewing published studies and the clinical management of patients. METHOD: Our review examined the published articles about the concept of metaverse roadmap, and four additional domains were extracted: education, prehospital and disaster medicine, diagnosis and treatment application, and administrative affairs. RESULTS: Augmented reality (AR) and virtual reality (VR) integration have broad applications in education and clinical training. VR-related studies surpassed AR-related studies in the emergency medicine field. The metaverse roadmap revealed that lifelogging and mirror world are still developing fields of the metaverse. CONCLUSION: Our findings provide insight into the features, application, development, and potential of a metaverse in emergency medicine. This study will enable emergency care systems to be better equipped to face future challenges.

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Objectives: The coronavirus disease 2019 (COVID-19) pandemic presented unprecedented challenges to healthcare systems worldwide. While existing studies on innovation have typically focused on technology, health providers still only have a vague understanding of the features of emergency responses during resource exhaustion in the early stage of a pandemic. Thus, a better understanding of innovative responses by healthcare systems during a crisis is urgently needed. Methods: Using content analysis, this narrative review examined articles on innovative responses during the COVID-19 pandemic that were published in 2020. Results: A total of 613 statements about innovative responses were identified from 296 articles and were grouped under the following thematic categories: medical care (n = 273), workforce education (n = 144), COVID-19 surveillance (n = 84), medical equipment (n = 59), prediction and management (n = 34), and governance (n = 19). From the four types of innovative responses extracted, technological innovation was identified as the major type of innovation during the COVID-19 pandemic, followed by process innovations, frugal innovation, and repurposing. Conclusion: Our review provides insights into the features, types, and evolution of innovative responses during the COVID-19 pandemic. This review can help health providers and society show better and quicker responses in resource-constrained conditions in future pandemics.

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Expert systems are frequently used to make predictions in various areas. However, the practical robustness of expert systems is not as good as expected, mainly due to the fact that finding an ideal system configuration from a specific dataset is a challenging task. Therefore, how to optimize an expert system has become an important issue of research. In this paper, a new method called the robust design-based expert system is proposed to bridge this gap. The technical process of this system consists of data initialization, configuration generation, a genetic algorithm (GA) framework for feature selection, and a robust mechanism that helps the system find a configuration with the highest robustness. The system will finally obtain a set of features, which can be used to predict a pandemic based on given data. The robust mechanism can increase the efficiency of the system. The configuration for training is optimized by means of a genetic algorithm (GA) and the Taguchi method. The effectiveness of the proposed system in predicting epidemic trends is examined using a real COVID-19 dataset from Japan. For this dataset, the average prediction accuracy was 60%. Additionally, 10 representative features were also selected, resulting in a selection rate of 67% with a reduction rate of 33%. The critical features for predicting the epidemic trend of COVID-19 were also obtained, including new confirmed cases, ICU patients, people vaccinated, population, population density, hospital beds per thousand, middle age, aged 70 or older, and GDP per capital. The main contribution of this paper is two-fold: Firstly, this paper has bridged the gap between the pandemic research and expert systems with robust predictive performance. Secondly, this paper proposes a feature selection method for extracting representative variables and predicting the epidemic trend of a pandemic disease. The prediction results indicate that the system is valuable to healthcare authorities and can help governments get hold of the epidemic trend and strategize their use of healthcare resources.