Background: Breast cancer is considered one of the most common cancers in women caused by various clinical, lifestyle, social, and economic factors. Machine learning has the potential to predict breast cancer based on features hidden in data. Objective: This study aimed to predict breast cancer using different machine-learning approaches applying demographic, laboratory, and mammographic data.Material and Methods: In this analytical study, the database, including 5,178 independent records, 25% of which belonged to breast cancer patients with 24 attributes in each record was obtained from Motamed cancer institute (ACECR), Tehran, Iran. The database contained 5,178 independent records, 25% of which belonged to breast cancer patients containing 24 attributes in each record. The random forest (RF), neural network (MLP), gradient boosting trees (GBT), and genetic algorithms (GA) were used in this study. Models were initially trained with demographic and laboratory features (20 features). The models were then trained with all demographic, laboratory, and mammographic features (24 features) to measure the effectiveness of mammography features in predicting breast cancer. Results: RF presented higher performance compared to other techniques (accuracy 80%, sensitivity 95%, specificity 80%, and the area under the curve (AUC) 0.56). Gradient boosting (AUC=0.59) showed a stronger performance compared to the neural network. Conclusion: Combining multiple risk factors in modeling for breast cancer prediction could help the early diagnosis of the disease with necessary care plans. Collection, storage, and management of different data and intelligent systems based on multiple factors for predicting breast cancer are effective in disease management.

Introduction. In recent years, the use of dashboards in healthcare has been considered an effective approach for the visual presentation of information to support clinical and administrative decisions. Effective and efficient use of dashboards in clinical and managerial processes requires a framework for the design and development of tools based on usability principles. Objectives. The present study is aimed at investigating the existing questionnaires used for the usability evaluation framework of dashboards and at presenting more specific usability criteria for evaluating dashboards. Methods. This systematic review was conducted using PubMed, Web of Science, and Scopus, without any time restrictions. The final search of articles was performed on September 2, 2022. Data collection was performed using a data extraction form, and the content of selected studies was analyzed based on the dashboard usability criteria. Results. After reviewing the full text of relevant articles, a total of 29 studies were selected according to the inclusion criteria. Regarding the questionnaires used in the selected studies, researcher‐made questionnaires were used in five studies, while 25 studies applied previously used questionnaires. The most widely used questionnaires were the System Usability Scale (SUS), Technology Acceptance Model (TAM), Situation Awareness Rating Technique (SART), Questionnaire for User Interaction Satisfaction (QUIS), Unified Theory of Acceptance and Use of Technology (UTAUT), and Health Information Technology Usability Evaluation Scale (Health‐ITUES), respectively. Finally, dashboard evaluation criteria, including usefulness, operability, learnability, ease of use, suitability for tasks, improvement of situational awareness, satisfaction, user interface, content, and system capabilities, were suggested. Conclusion. General questionnaires that were not specifically designed for dashboard evaluation were mainly used in reviewed studies. The current study suggested specific criteria for measuring the usability of dashboards. When selecting the usability evaluation criteria for dashboards, it is important to pay attention to the evaluation objectives, dashboard features and capabilities, and context of use.

Background: Breast cancer diagnostic data is complex and accompanied by noise. Quantum machine learning can enhance the accuracy, efficiency, and scalability of artificial intelligence algorithms and has applications in various fields such as drug discovery and personalized medicine. Methods: In the systematic review conducted, the databases PubMed, Embase, Scopus, and Web of Science were searched in December 2024. The search strategy included the keywords "Breast Cancer," "Artificial Intelligence," and "Quantum machine learning" along with their synonyms in article titles. Descriptive, qualitative, review, and non-English studies were excluded. The qualitative evaluation of the articles and the assessment of their bias were determined based on the Joanna Briggs Institute (JBI) indicators checklist. Results: Twenty-nine studies utilizing artificial intelligence models for personalized breast cancer management were selected. Seventeen studies employing various deep learning methods achieved satisfactory results in predicting treatment response and prognosis, effectively contributing to the personalized management of breast cancer. Twenty-six studies demonstrated that machine learning methods could enhance the processes of classification, screening, diagnosis, and prognosis of breast cancer. The methods most frequently used in modeling were quantum support vector machine (QSVM), quantum convolutional neural network (QCNN), and quantum neural network (QNN), with an average AUC of 0.91. Additionally, the average accuracy, sensitivity, specificity, and precision indices of the models ranged from 90% to 96%. Conclusion: Quantum computing can address some challenges arising from the increasing complexity and size of artificial intelligence models. Overall, the combination of artificial intelligence and quantum computing can significantly accelerate the drug discovery process and the development of effective drugs.

Introduction: Breast cancer rates have been increasing worldwide, particularly among young women, suggesting important interactions between genes and health behaviors. At the same time, mobile technology, including smartphones applications (apps), has emerged as a new tool for delivering healthcare and health-related services. In 2019, there were nearly 670 publicly available breast cancer apps designed to provide disease and treatment information, to manage disease, and raise overall awareness.Material and Methods: In order to conduct a review, the Medline, Scopus and PubMed databases were searched with the keywords "mobile health", "mobile health in electronics health", "breast cancer and electronic health"," mobile health and breast cancer"," mobile health and breast cancer qualify life" and their equivalent. Out of the 60 articles found, after the depth of the criteria Inclusion in the study, 16 articles remained, which were reviewed and given using PRISMA 2020 checklist. SPSS software v.22 was used for description analysis.Results: A total of 16 articles met the inclusion criteria and were included in this review. All studies have determined the positive impact of applications on cancer detection and clinical health outcomes. In addition, more than half of mobile applications have multiple functions, such as providing information, planning and education. Furthermore, most studies examining patient satisfaction and quality improvement have shown that users of healthcare applications are significantly more satisfied with life, leading to better quality.Conclusion: The evidence of the studies which are included in this systematic review is currently limited, but it suggests that the mobile apps might be an acceptable information source for women with breast cancer and lead to improved patient well-being.

Background and Aims: Infection with Covid‐19 disease can lead to mortality in a short time. Early prediction of the mortality during an epidemic disease can save patients' lives through taking timely and necessary care interventions. Therefore, predicting the mortality of patients with Covid‐19 using machine learning techniques can be effective in reducing mortality rate in Covid‐19. The aim of this study is to compare four machine‐learning algorithm for predicting mortality in Covid‐19 disease.Methods: The data of this study were collected from hospitalized patients with COVID‐19 in five hospitals settings in Tehran (Iran). Database contained 4120 records, about 25% of which belonged to patients who died due to Covid‐19. Each record contained 38 variables. Four machine‐learning techniques, including random forest (RF), regression logistic (RL), gradient boosting tree (GBT), and support vector machine (SVM) were used in modeling.Results: GBT model presented higher performance compared to other models (accuracy 70%, sensitivity 77%, specificity 69%, and the ROC area under the curve 0.857). RF, RL, and SVM models with the ROC area under curve 0.836, 0.818, and 0.794 were in the second and third places.Conclusion: Considering the combination of multiple influential factors affecting death Covid‐19 can help in early prediction and providing a better care plan. In addition, using different modeling on data can be useful for physician in providing appropriate care.

Importance Assisted Reproductive Technologies have been developed to address infertility by improving embryo selection. Artificial intelligence (AI), using Time-Lapse Imaging, enhances predictions from fertilization to the blastocyst stage. Objective Studies show that AI can identify suitable embryos more effectively than specialists. It improves IVF success rates by enhancing embryo transfer success and reducing miscarriage risks. With IVF success rates below 40%, it is essential to explore AI methods to boost outcomes. Findings A systematic review in October 2024 searched databases like PubMed and Scopus using terms related to IVF and AI, excluding non-English and qualitative studies. Twenty-seven studies were reviewed; 17 predicted treatment responses with deep learning. Two studies used neural networks for successful treatment prediction, and eight employed ML methods such as NB, SVM, and RF, with an average AUC of 0.91. Models showed 90–96% accuracy, sensitivity, and precision. Conclusion AI technologies, particularly NB and Reinforcement Learning, show promise in improving IVF outcomes by enhancing classification and diagnosis while saving time. Interdisciplinary approaches using micro and Nano-biotechnology can help overcome clinical challenges. Relevance Examining the quality of sperm and egg separately using AI could further improve fertility testing and success in ART, optimizing clinical results.