Objectives To evaluate emergency medical services (EMS) delays and their impact on time to treatment and mortality in patients with ST-elevation myocardial infarction (STEMI). Method We collected data on EMS time intervals from published studies across five electronic databases. The primary EMS interval was the time in minutes between first medical contact and arrival at hospital door (FMC-to-door time). Secondary intervals were other components of EMS delay. Weighted means were measured using random-effects models. Meta-regression was used to identify factors associated with EMS delays and to assess the impact of EMS delay on the proportion of patients treated within90 min and mortality. Results Two independent reviewers included 100 studies (125 343 patients) conducted in 20 countries. The weighted mean FMC-to-door time was 41 min (n=101 646; 95% CI 39 to 43, range 21–88). However, substantial heterogeneity was observed with each interval, which could be explained by region and urban classification, distance to hospital and method of ECG interpretation. In a meta-regression adjusted for door-to-balloon time, a 10 min increase in FMC-to-door time was associated with a 10.6% (95% CI 7.6% to 13.5%; p<0.001) reduction in the proportion of patients treated within 90 min. Shorter EMS delay was significantly associated with lower short-term mortality in patients receiving prehospital thrombolysis (p=0.018). Conclusion EMS delays account for half of the total system delay in STEMI. There is a fourfold global variation in EMS delays, which are not completely explained by differences in system characteristics. Reducing unexplained variation could yield improvements in the time to treatment and outcome of STEMI patients. PROSPERO registration number CRD42017074118.

Objectives To explore the relationship between emergency medical service (EMS) delay time, overall time to reperfusion and clinical outcome in patients with ST-elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PPCI). Methods This was a retrospective observational study of 2976 patients with STEMI who presented to EMS and underwent PPCI between January 2014 and December 2017. We performed multivariable logistic models to assess the relationship between EMS delay time and 30-day mortality and to identify factors associated with system delay time. Results EMS delay time accounted for the first half of total system delay (median=59 min (IQR=48–77)). Compared with those who survived, those who died had longer median EMS delay times (59 (IQR=11–74) vs 74 (IQR=57–98), p<0.001). EMS delay time was independently associated with a higher risk of mortality (adjusted OR 1.20; 95% CI 1.02 to 1.40, for every 30 min increase), largely driven by complicated patients with cardiogenic shock or cardiac arrest. Independent predictors of longer EMS delay times were older age, women, cardiogenic shock, cardiac arrest, prehospital notification and intensive care management. Although longer EMS delay times were associated with shorter door-to-balloon times, total system delay times increased with increasing EMS delay times. Conclusion Increasing EMS delay times, particularly those result from haemodynamic complications, increase total time to reperfusion and are associated with 30-day mortality after STEMI. All efforts should be made to monitor and reduce EMS delay times for timely reperfusion and better outcome.

Background/Objectives: The increasing application of artificial intelligence (AI) and machine learning (ML) in health and medicine has attracted a great deal of research interest in recent decades. This study aims to provide a global and historical picture of research concerning AI and ML in health and medicine. Methods: We used the Scopus database for searching and extracted articles published between 2000 and 2024. Then, we generated information about productivity, citations, collaboration, most impactful research topics, emerging research topics, and author keywords using Microsoft Excel 365 and VOSviewer software (version 1.6.20). Results: We retrieved a total of 22,113 research articles, with a notable surge in research activity in recent years. Core journals were Scientific Reports and IEEE Access, and core institutions included Harvard Medical School and the Ministry of Education of the People’s Republic of China, while core countries comprised the United States, China, India, the United Kingdom, and Saudi Arabia. Citation trends indicated substantial growth and recognition of AI’s and ML impact on health and medicine. Frequent author keywords identified key research hotspots, including specific diseases like Alzheimer’s disease, Parkinson’s diseases, COVID-19, and diabetes. The author keyword analysis identified “deep learning”, “convolutional neural network”, and “classification” as dominant research themes. Conclusions: AI’s transformative potential in AI and ML in health and medicine holds promise for improving global health outcomes.

Objective: The aim of this study was to summarize the literature on the applications of machine learning (ML) and their performance in Emergency Medical Services (EMS).Methods: Four relevant electronic databases were searched (from inception through January 2024) for all original studies that employed EMS-guided ML algorithms to enhance the clinical and operational performance of EMS. Two reviewers screened the retrieved studies and extracted relevant data from the included studies. The characteristics of included studies, employed ML algorithms, and their performance were quantitively described across primary domains and subdomains.Results: This review included a total of 164 studies published from 2005 through 2024. Of those, 125 were clinical domain focused and 39 were operational. The characteristics of ML algorithms such as sample size, number and type of input features, and performance varied between and within domains and subdomains of applications. Clinical applications of ML algorithms involved triage or diagnosis classification (n = 62), treatment prediction (n = 12), or clinical outcome prediction (n = 50), mainly for out-of-hospital cardiac arrest/OHCA (n = 62), cardiovascular diseases/CVDs (n = 19), and trauma (n = 24). The performance of these ML algorithms varied, with a median area under the receiver operating characteristic curve (AUC) of 85.6%, accuracy of 88.1%, sensitivity of 86.05%, and specificity of 86.5%. Within the operational studies, the operational task of most ML algorithms was ambulance allocation (n = 21), followed by ambulance detection (n = 5), ambulance deployment (n = 5), route optimization (n = 5), and quality assurance (n = 3). The performance of all operational ML algorithms varied and had a median AUC of 96.1%, accuracy of 90.0%, sensitivity of 94.4%, and specificity of 87.7%. Generally, neural network and ensemble algorithms, to some degree, out-performed other ML algorithms.Conclusion: Triaging and managing different prehospital medical conditions and augmenting ambulance performance can be improved by ML algorithms. Future reports should focus on a specific clinical condition or operational task to improve the precision of the performance metrics of ML models.

Magnetic Resonance Imaging (MRI) is increasingly becoming a cornerstone in modern diagnostic healthcare, offering unparalleled capabilities in stroke, dementia, and cancer screening. Therefore, this study aims to map medical MRI literature affiliated with Arab countries, focusing on publication trends, top journals, author affiliations, study countries, and authors' collaboration, and keyword analysis. The scientific database used is the Scopus database. Microsoft Excel, VOSviewer software, and Biblioshiny for the Bibliometrix R package are the bibliometric tools used in this analysis. A total of 2592 publications were published between 1988 and 2022, with total citations of 22,115. Most of them were original articles (91,7%) and 89.9% were published in traditional journals. The number of total publications exhibited a steady increase over time, whereas total citations showed fluctuations, peaking in 2015 with 1571 citations for publications from that year. The most cited article was authored by Yaseen M. Arabi, receiving 286 citations. Saudi Arabia was the top active country. In addition, the most prolific author was Maha S Zaki, and the most prolific source was the “Egyptian Journal of Radiology and Nuclear Medicine”. The most prolific affiliation was Cairo University. The “multiple sclerosis” and “case report” were the most trending keywords. The analysis revealed a significant growth in MRI research inside Arab countries, as shown by an increase in the total number of publications and international collaborations. Despite these developments, the results of this study suggest that there is still room for MRI research in the Arab region to advance. This can be achieved through increasing international collaboration and multidisciplinary work.