Esmaeil Mehraeen

Introduction: The COVID-19 pandemic has surrogated the world and enforced a massive burden. Several risk factors, such as immunosuppression state, were recognized for severe COVID-19. Human immunodeficiency virus (HIV) is an immunosuppressing virus that deteriorates people living with HIV...

Introduction: Standardizing patient data collection and analysis facilitates accurate reporting and enhances communication among healthcare teams. Given the high prevalence of leishmaniasis in specific geographic regions of Iran and the critical role of registry systems, this study aimed to identify the essential data elements required for developing a comprehensive registry system for patients with leishmaniasis. Methods: This descriptive-applied study was conducted in 2024 in two phases: a literature review (2011-2024) to identify data elements for leishmaniasis, followed by expert consultation to prepare an initial draft of the minimum data set. Results: A total of 60 data elements with a content validity ratio (CVR) ≥ 0.75 were confirmed and organized into 10 subgroups for the comprehensive leishmaniasis registry. The subgroups comprised: geographic location of registration; identity and demographic information; travel and changes of residence; characteristics of the patient’s dwelling; medical history and current medications; prior history of leishmaniasis; disease-specific information; laboratory data; therapeutic interventions; and follow-up of treatment discontinuation. Conclusion: Considering the pivotal role of registry systems in disease management, prevention, treatment, and resource allocation, designing a comprehensive registry—particularly for endemic and region-specific diseases such as leishmaniasis is essential.

Objective: The current study aims to collect and perform an umbrella review including articles discussing AI’s significant impact on the healthcare field, encompassing progress in diagnostics, treatment, ethical considerations, and practical implementations. Material and Methods: An extensive search was performed on four key online databases: Web of Science, PubMed, Scopus, and Embase. The exploration extended up to Jan/03/2024. Inclusion criteria required articles to be systematic reviews, peer-reviewed, and focused on emerging trends of AI in healthcare and medicine. Studies were excluded if they involved non-human subjects, lacked essential data, were duplicates, or were categorized as narrative or umbrella reviews. Results: The application of AI in healthcare has shown significant clinical benefits, especially during the COVID-19 pandemic. AI technologies have been essential in genomic sequencing and the development of vaccines and therapeutics. AI supports efficient resource allocation through predictive models forecasting disease spread and healthcare demand. It optimizes clinical workflows, enhances the precision of prognostic tools, and enables contactless care, reducing the risk of healthcare workers’ occupational exposure. Conclusion: AI in healthcare, offering significant opportunities to enhance diagnostic accuracy, resource allocation, and personalized care, while also data privacy concerns, validation issues, and resistance to adoption.

Objectives: Ontologies play a crucial role in healthcare systems due to the diversity of concepts, roles, users, and diagnostic and therapeutic methods. They facilitate the development of knowledge bases and the sharing and representation of information. With the integration of artificial intelligence (AI) into healthcare, ontologies can serve as complementary tools to enhance the quality of services.Methods: This review study examines existing research on the application of ontologies in AI systems within the healthcare industry. By analyzing their applications, benefits, challenges, and limitations, the study seeks to provide a deeper understanding of their impact on advancing AI technologies and improving healthcare processes. In addition, the study offers recommendations for strengthening the development and use of ontologies in intelligent healthcare systems.Results: The findings of this review indicate that ontologies enhance the accuracy of results and support medical decision-making by enabling the semantic exchange of diverse and heterogeneous data. They are essential for the development of decision support systems and for fostering intelligent interactions between patients and healthcare systems. Furthermore, ontologies contribute to healthcare decision-making by semantically analyzing the connections between diseases, geographic regions, and environmental factors.Conclusions: The use of ontologies in healthcare improves data analysis, patient diagnosis, treatment, and decision-making. Ontologies enhance data inference and interoperability in AI systems through data modeling, concept relationship extraction, knowledge enrichment, and information sharing. Given the vast scope of the healthcare domain, the diversity of specialties and data, and the absence of a dedicated ontology development methodology specific to this field, there is a clear need for a tailored and robust methodology.

Purpose: This paper highlights the transformative impact of information and communication technologies (ICTs) on education, focusing on their potential to revolutionize teaching methods. It also explores the global trend of integrating technology-based learning into educational curricula. Design/methodology/approach: A comprehensive literature search, using combinations of relevant keywords such as educational technology, learning technology, curriculum, and medical curriculum, was conducted in PubMed/MEDLINE, Embase, and Scopus databases for articles published in English until July 24, 2023. We appraised the quality and bias risk of included studies using the National Institute of Health (NIH) quality assessment tool. Findings: The study’s findings highlight the significant impact of integrating new technologies into medical education. The selection of 56 articles that focused on implementing new curricula and modules in medical training provided a comprehensive analysis of how virtual patient simulations, 3D modeling software, augmented reality, and video conferencing platforms enhanced learning and clinical skills. Integrating these technologies yielded promising outcomes, including improved knowledge retention, self-directed learning, and increased motivation among students. This evolution of educational technology in medical education indicates a clear shift towards more interactive, personalized, and advanced digital learning experiences. The incorporation of simulation, Artificial Intelligence (AI), Virtual Reality (VR), and mobile technologies has not only transformed the learning process for medical students but also influenced the way professionals engage with educational content, ultimately enhancing patient care and medical practice. Originality/value: Integrating learning technologies into the education curriculum provides valuable opportunities to enhance medical students' capabilities, self-confidence, and knowledge. Also, learning technologies provide practical applications such as producing audio and video films about nasogastric intubation, online recording of students' feedback, full-body simulator and guided trainer, live demonstrations, and covering general surgery by audio-visual podcasts.

Background: As medical education evolves, incorporating innovative teaching methods is crucial for developing nursing students’ critical thinking and problem-solving skills. Game-based learning (GBL) has gained popularity, engaging students through immersive experiences and allowing personalized learning. Objective: This systematic review aimed to investigate the impact of educational games on outcomes of clinical nursing skills. Methods: In this study, the authors systematically searched the 4 public databases (PubMed, Embase, Scopus, and Web of Science) to investigate the role of educational games in improving the clinical skills of nursing students. This paper is based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. We also checked the bias risk of selected studies by the Newcastle-Ottawa Scale (NOS) bias assessment tool. Results: In this study, 801 articles were initially retrieved using a specified search strategy, with 38 remaining after applying inclusion and exclusion criteria. The final included studies published between 2017 and 2023 spanned various countries and focused on diverse learning objectives. A broad range of learning objectives, such as developing diagnostic reasoning, enhancing knowledge and cognitive skills, and improving training methods, can be supported by a game-based platform. We also showed that while many games used web-based platforms, few were conducted in person, and some were developed in app formats for smartphones. Conclusions: GBL is transforming nursing education by enhancing student engagement and clinical skills through immersive experiences. Despite its advantages, GBL faces challenges such as development costs and the effect of expertise reversal. Developing standardized assessment tools will help unify definitions and improve the comparability of research findings, ultimately enhancing the evidence base for GBL’s effectiveness.

COVID-19 transmission can be influenced by various factors, including weather and climate conditions, population density, and the availability of medical facilities. To gain a deeper understanding of this topic, an in-depth analysis of recent studies is needed. Our objective was to investigate previous systematic reviews that have examined the seasonal variation of COVID-19 and the impact of climate on its transmission and mortality. Online databases that included PubMed, Scopus, Web of Science, and Cochrane were searched using relevant keywords up to November 2021. Negative associations were found between temperature and COVID-19 spread and mortality (6/9 studies, 66.6%). These negative correlations imply a decrease in the spread and mortality of COVID-19 with an increase in temperature. Similarly, seven systematic reviews reported a negative correlation between humidity and transmission or mortality of COVID-19 (7/9 studies, 77.7%). COVID-19 spread was not associated with precipitation (three studies) but was negatively correlated with sunlight or UV radiation (two studies), COVID-19 incidence and mortality were positively associated with wind speed (one study). One study reported that the effect of air pressure and UV radiation on COVID-19 activity was unknown. The effects of air pollution, seasonal changes, wind speed, precipitation, and UV radiation on COVID-19 incidence or mortality remain unclear. However, factors proposed as having the greatest influence on COVID-19 incidence or mortality include air pollution, wind speed, and wastewater. Sunlight exposure and warm climates likely assist with reducing COVID-19 incidence or mortality, with the infection having a winter seasonality.