Background. In todays industrialized world, coronary artery disease (CAD) is one of the leading causes of death, and early detection and timely intervention can prevent many of its complications and eliminate or reduce the resulting mortality. Machine learning (ML) methods as one of the cutting-edge technologies can be used as a suitable solution in diagnosing this disease. Methods. In this study, different ML algorithms performances were compared for their effectiveness in developing a model for early CAD diagnosis based on clinical examination features. This applied descriptive study was conducted on 303 records and overall 26 features, of which 26 were selected as the target features with the advice of several clinical experts. In order to provide a diagnostic model for CAD, we ran most of the most critical classification algorithms, including Multilayer Perceptron (MLP), Support Vector Machine (SVM), Logistic Regression (LR), J48, Random Forest (RF), K-Nearest Neighborhood (KNN), and Naive Bayes (NB). Seven different classification algorithms with 26 predictive features were tested to cover all feature space and reduce model error, and the most efficient algorithms were identified by comparison of the results. Results. Based on the compared performance metrics, SVM (AUC=0.88, F-measure=0.88, ROC=0.85), and RF (AUC=0.87, F-measure=0.87, ROC=0.91) were the most effective ML algorithms. Among the algorithms, the KNN algorithm had the lowest efficiency (AUC=0.81, F-measure=0.81, ROC=0.77). In the diagnosis of coronary artery disease, machine learning algorithms have played an important role. Proposed ML models can provide practical, cost-effective, and valuable support to doctors in making decisions according to a good prediction. Discussion. It can become the basis for developing clinical decision support systems. SVM and RF algorithms had the highest efficiency and could diagnose CAD based on patient examination data. It is suggested that further studies be performed using these algorithms to diagnose coronary artery disease to obtain more accurate results.

Introduction. Coronary artery disease (CAD) is one of the main causes of death all over the world. One way to reduce the mortality rate from CAD is to predict its risk and take effective interventions. The use of machine learning‐ (ML‐) based methods is an effective method for predicting CAD‐induced death, which is why many studies in this field have been conducted in recent years. Thus, this study aimed to review published studies on artificial intelligence classification algorithms in CAD detection and diagnosis. Methods. This study systematically reviewed the most cutting‐edge techniques for analyzing clinical and paraclinical data to quickly diagnose CAD. We searched PubMed, Scopus, and Web of Science databases using a combination of related keywords. A data extraction form was used to collect data after selecting the articles based on inclusion and exclusion criteria. The content analysis method was used to analyze the data, and based on the study’s objectives, the results are presented in tables and figures. Results. Our search in three prevalent databases resulted in 15689 studies, of which 54 were included to be reviewed for data analysis. Most studies used laboratory and demographic data classification and have shown desirable results. In general, three ML methods (traditional ML, DL/NN, and ensemble) were used. Among the algorithms used, random forest (RF), linear regression (LR), neural networks (NNs), support vector machine (SVM), and K‐nearest networks (KNNs) have the most applications in the field of code recognition. Conclusion. The findings of this study show that these models based on different ML methods were successful despite the lack of a benchmark for comparing and analyzing ML features, methods, and algorithms in CAD diagnosis. Many of these models performed better in their analyses of CAD features as a result of a closer look. In the near future, clinical specialists can use ML‐based models as a powerful tool for diagnosing CAD more quickly and precisely by looking at its design’s technical facets. Among its incredible outcomes are decreased diagnostic errors, diagnostic time, and needless invasive diagnostic tests, all of which typically result in decreases in diagnostic expenses for healthcare systems.

Male infertility contributes to 20–30% of infertility cases, yet traditional diagnostic and treatment methods face limitations in accuracy and consistency. Artificial intelligence (AI) promises to transform male infertility management within in vitro fertilization (IVF) by enhancing precision and efficiency. This study aims to map current AI applications in male infertility, evaluate their performance in IVF contexts, identify gaps in research, and propose strategies for clinical adoption. We conducted a mapping review of 14 studies, sourced from PubMed, Scopus, IEEE, and Web of Science up to 2024. Using PRISMA guidelines, we systematically searched titles and abstracts with keywords like "IVF," "AI," and "sperm analysis." Two authors independently screened records, extracted data on AI techniques, sample sizes, and outcomes, and categorized applications through content analysis, resolving discrepancies via consensus. AI employs tools like support vector machines (SVM), multi-layer perceptrons (MLP), and deep neural networks across six key areas. These include sperm morphology (e.g., SVM with AUC 88.59% on 1400 sperm), motility (e.g., SVM with 89.9% accuracy on 2817 sperm), and non-obstructive azoospermia (NOA) sperm retrieval (e.g., gradient boosting trees [GBT] with AUC 0.807 and 91% sensitivity on 119 patients). AI also predicts IVF success (e.g., random forests with AUC 84.23% on 486 patients) and assesses sperm DNA fragmentation. Research surged since 2021, with 8 of 14 studies (57%) published between 2021 and 2023, reflecting growing interest.. AI enhances diagnostic accuracy and treatment outcomes in male infertility. Future steps include multicenter validation trials, AI-driven sperm selection for IVF/ICSI, and standardized methods to ensure clinical reliability. Addressing ethical concerns like data privacy will further enable AI to improve IVF success globally.

Introduction: The use of telemedicine in orthopaedics can provide high-quality orthopaedic services to patients in remote areas. Tele-orthopaedics is widely acknowledged for decreasing travel, time and cost, increasing accessibility and quality of care. In the absence of a comprehensive review on tele-orthopaedics applications and services, here, we systematically identify and classify the tele-orthopaedic applications and services, and provide an overview of the trends in the field. Methods: In this study, a systematic mapping was conducted to answer six research questions, we searched the databases Scopus, PubMed, IEEE Digital Library and Web of Science up to 2019. Consequently, 77 papers were screened and selected on the basis of specific inclusion and exclusion criteria. Results: We found that mobile-based teleconsultation was mostly asynchronous, while non-mobile teleconsultation was synchronous. The results showed that the physician–patient relationship was more common than other interactions, such as physician–physician and physician–robot interactions. In addition, more than half of the services provided by tele-orthopaedics have been used for orthopaedic diseases/traumas in which joint replacement and fracture reduction have been the most important orthopaedic procedures. It has been noted that more attention has been paid to tele-orthopaedics in developed countries such as the USA, Australia, Canada and Finland. Discussion: Telemonitoring (teleconsultation and telemetry) and telesurgery (telerobotics and telementoring) were found to be the two major forms of tele-orthopaedics. Mobile phones were used asynchronously in most of the teleconsultations. The development of different applications may result in the use of multiple smartphones applications in real-time teleconsultation. The use of smartphones is expected to increase in the near future.