Application of artificial intelligence in chronic myeloid leukemia (CML) disease prediction and management: a scoping review.

BACKGROUND: Navigating the complexity of chronic myeloid leukemia (CML) diagnosis and management poses significant challenges, including the need for accurate prediction of disease progression and response to treatment. Artificial intelligence (AI) presents a transformative approach that enables the development of sophisticated predictive models and personalized treatment strategies that enhance early detection and improve therapeutic interventions for better patient outcomes. METHODS: An extensive search was conducted to retrieve relevant articles from PubMed, Scopus, and Web of Science databases up to April 24, 2023. Data were collected using a standardized extraction form, and the results are presented in tables and graphs, showing frequencies and percentages. The authors adhered to the PRISMA-ScR checklist to ensure transparent reporting of the study. RESULTS: Of the 176 articles initially identified, 12 were selected for our study after removing duplicates and applying the inclusion and exclusion criteria. AI's primary applications of AI in managing CML included tumor diagnosis/classification (n = 9, 75%), prediction/prognosis (n = 2, 17%), and treatment (n = 1, 8%). For tumor diagnosis, AI is categorized into blood smear image-based (n = 5), clinical parameter-based (n = 2), and gene profiling-based (n = 2) approaches. The most commonly employed AI models include Support Vector Machine (SVM) (n = 5), eXtreme Gradient Boosting (XGBoost) (n = 4), and various neural network methods, such as Artificial Neural Network (ANN) (n = 3). Furthermore, Hybrid Convolutional Neural Network with Interactive Autodidactic School (HCNN-IAS) achieved 100% accuracy and sensitivity in organizing leukemia data types, whereas MayGAN attained 99.8% accuracy and high performance in diagnosing CML from blood smear images. CONCLUSIONS: AI offers groundbreaking insights and tools for enhancing prediction, prognosis, and personalized treatment in chronic myeloid leukemia. Integrated AI systems empower healthcare practitioners with advanced analytics, optimizing patient care and improving clinical outcomes in CML management.

Efficacy of virtual reality-based training programs and games on the improvement of cognitive disorders in patients: a systematic review and meta-analysis.

INTRODUCTION: Cognitive impairments present challenges for patients, impacting memory, attention, and problem-solving abilities. Virtual reality (VR) offers innovative ways to enhance cognitive function and well-being. This study explores the effects of VR-based training programs and games on improving cognitive disorders. METHODS: PubMed, Scopus, and Web of Science were systematically searched until May 20, 2023. Two researchers selected and extracted data based on inclusion and exclusion criteria, resolving disagreements through consultation with two other authors. Inclusion criteria required studies of individuals with any cognitive disorder engaged in at least one VR-based training session, reporting cognitive impairment data via scales like the MMSE. Only English-published RCTs were considered, while exclusion criteria included materials not primarily focused on the intersection of VR and cognitive disorders. The risk of bias in the included studies was assessed using the MMAT tool. Publication bias was assessed using funnel plots and Egger's test. The collected data were utilized to calculate the standardized mean differences (Hedges's g) between the treatment and control groups. The heterogeneity variance was estimated using the Q test and I2 statistic. The analysis was conducted using Stata version 17.0. RESULTS: Ten studies were included in the analysis out of a total of 3,157 retrieved articles. VR had a statistically significant improvement in cognitive impairments among patients (Hedges's g = 0.42, 95% CI: 0.15, 0.68; p_value = 0.05). games (Hedges's g = 0.61, 95% CI: 0.30, 0.39; p_value = 0.20) had a more significant impact on cognitive impairment improvement compared to cognitive training programs (Hedges's g = 0.29, 95% CI: -0.11, 0.69; p_value = 0.24). The type of VR intervention was a significant moderator of the heterogeneity between studies. CONCLUSION: VR-based interventions have demonstrated promise in enhancing cognitive function and addressing cognitive impairment, highlighting their potential as valuable tools in improving care for individuals with cognitive disorders. The findings underscore the relevance of incorporating virtual reality into therapeutic approaches for cognitive disorders.

Health challenges and facilitators of arbaeen pilgrimage: a scoping review.

BACKGROUND: The Arbaeen Pilgrimage, a momentous religious journey drawing millions of participants annually, presents a profound spiritual experience. However, amidst its significance lie various health challenges that pilgrims encounter along the way. Addressing these challenges is vital to ensure the well-being of participants and the success of this extraordinary event. In light of this, the aim of this study is to examine the health challenges of the Arbaeen Pilgrimage, identify facilitators for solving these challenges, and propose effective solutions to enhance the overall pilgrimage experience for all involved. METHODS: The scoping review was performed by searching databases such as Web of Science, PubMed, Scopus, and Google Scholar search engine with a focus on the keywords "Arbaeen", "Arbaeen walk" and "Arbaeen pilgrimage". The search was not constrained by a specific time limitation in the databases. Data from studies were extracted using a data extraction form consisting of 9 fields. The selection of articles and data extraction were carried out by two researchers, adhering to predefined inclusion and exclusion criteria. Any disagreements were resolved through consultation with a third researcher. The study was reported following the PRISMA checklist. RESULTS: Out of 1619 retrieved articles, 9 were finally included in this study. All these studies were published since 2017 and conducted in Iraq and Iran. In total, 101 health challenges and facilitators were identified, comprising 61 challenges and 40 facilitators. The challenges with the highest frequency included "infectious disease outbreaks" (n = 7), "Poor management of Iraq's health system in waste collection and disposal" (n = 4), "Rising incidence of walking injuries among pilgrims (e.g., burns, fractures, lacerations, wounds, and blisters)" (n = 4), and "Insufficient knowledge about personal and public health"(n = 4). The most important facilitators to solving the challenges were: "Customized pilgrim training and addressing their issues, with a focus on vital practices" (n = 6), "Coordinating mass gathering stakeholders, including health ministries and organizations" (n = 4), and "Implementing an agile syndromic system for rapid surveillance and identification of contagious illnesses" (n = 4). CONCLUSION: The article discusses health challenges faced during the Arbaeen Pilgrimage and proposes facilitative measures for participants' well-being. It emphasizes the significance of addressing health risks in large gatherings and suggests incorporating measures for a safer and enjoyable pilgrimage experience. Overall, understanding and managing these health factors can lead to a successful execution of the Arbaeen Pilgrimage, benefiting the physical and spiritual well-being of all involved.

Barriers and facilitators of using health information technologies by women: a scoping review.

BACKGROUND AND AIM: Health information technologies play a vital role in addressing diverse health needs among women, offering a wide array of services tailored to their specific requirements. Despite the potential benefits, the widespread utilization of these technologies by women faces numerous barriers and challenges. These barriers can cause women to either reduce their usage of health technologies or refrain from using them altogether. Therefore, this review was done with the aim of identifying and classifying barriers and facilitators. METHODS: Some databases, including PubMed, Web of Sciences, and Scopus were searched using related keywords. Then, according to the inclusion and exclusion criteria, the articles were evaluated and selected. Finally, the barriers and facilitators were identified and classified. RESULTS: Out of 14,399 articles, finally 35 articles were included in the review. In general, 375 barriers (232 items) and facilitators (143 items) were extracted from the studies. After merging similar items, 121 barriers (51 items) and facilitators (70 items) identified were organized into five main themes (management, technological, legal and regulatory, personal, and data and information management). The most important barriers were "privacy, confidentiality, and security concerns" (n = 24), "deficiencies and limitations of infrastructure, software, hardware, and network" (n = 19), "sociocultural challenges" (n = 15), and "poor economic status" (n = 15). Moreover, the most important facilitators were "increasing awareness, skills and continuous education of women" (n = 17, in personal theme), "providing training services" (n = 14, in management theme), "simple, usable, and user-friendly design of technologies" (n = 14, in technological theme), and "providing financial or non-financial incentives (motivation) for women" (n = 14, in personal theme). CONCLUSION: This review showed that in order to use technologies, women face many barriers, either specific to women (such as gender inequality) or general (such as lack of technical skills). To overcome these barriers, policymakers, managers of organizations and medical centers, and designers of health systems can consider the facilitators identified in this review.

Design and development of a mobile-based self-care application for patients with depression and anxiety disorders.

BACKGROUND AND AIM: Depression and anxiety can cause social, behavioral, occupational, and functional impairments if not controlled and managed. Mobile-based self-care applications can play an essential and effective role in controlling and reducing the effects of anxiety disorders and depression. The aim of this study was to design and develop a mobile-based self-care application for patients with depression and anxiety disorders with the goal of enhancing their mental health and overall well-being. MATERIALS AND METHODS: In this study we designed a mobile-based application for self -management of depression and anxiety disorders. In order to design this application, first the education- informational needs and capabilities were identified through a systematic review. Then, according to 20 patients with depression and anxiety, this education-informational needs and application capabilities were approved. In the next step, the application was designed. RESULTS: In the first step, 80 education-information needs and capabilities were identified. Finally, in the second step, of 80 education- informational needs and capabilities, 68 needs and capabilities with a mean greater than and equal to 3.75 (75%) were considered in application design. Disease control and management, drug management, nutrition and diet management, recording clinical records, communicating with physicians and other patients, reminding appointments, how to improve lifestyle, quitting smoking and reducing alcohol consumption, educational content, sedation instructions, introducing health care centers for depression and anxiety treatment and recording activities, personal goals and habits in a diary were the most important features of this application. CONCLUSION: The designed application can encourage patients with depression and stress to perform self-care processes and access necessary information without searching the Internet.

Patients' perspectives and preferences toward telemedicine versus in-person visits: a mixed-methods study on 1226 patients.

INTRODUCTION: Despite the fact that telemedicine can eliminate geographical and time limitations and offer the possibility of diagnosing, treating, and preventing diseases by sharing reliable information, many individuals still prefer to visit medical centers for in-person consultations. The aim of this study was to determine the level of acceptance of telemedicine compared to in-person visits, identify the perceived advantages of telemedicine over in-person visits, and to explore the reasons why patients choose either of these two types of visits. METHODS: We developed a questionnaire using the rational method. The questionnaire consisted of multiple-choice questions and one open-ended question. A total of 2059 patients were invited to participate in the study. Chi-square tests and descriptive statistics were employed for data analysis. To analyze the data from the open-ended question, we conducted qualitative content analysis using MAXQDA 18. RESULTS: Out of the 1226 participants who completed the questionnaire, 865 (71%) preferred in-person visits, while 361 (29%) preferred telemedicine. Factors such as education level, specific health conditions, and prior experience with telemedicine influenced the preference for telemedicine. The participants provided a total of 183 different reasons for choosing either telemedicine (108 reasons) or in-person visits (75 reasons). Avoiding infectious diseases, saving cost, and eliminating and overcoming geographical distance barriers were three primary telemedicine benefits. The primary reasons for selecting an in-person visit were: more accurate diagnosis of the disease, more accurate and better examination of the patient by the physician, and more accurate and better treatment of the disease. CONCLUSION: The results demonstrate that despite the numerous benefits offered by telemedicine, the majority of patients still exhibit a preference for in-person visits. In order to promote broader acceptance of telemedicine, it becomes crucial for telemedicine services to address patient preferences and concerns effectively. Employing effective change management strategies can aid in overcoming resistance and facilitating the widespread adoption of telemedicine within the population.

The Impact of Health Technologies on Ostomy Care: A Systematic Review of Health Technologies Impact on Ostomy Care.

PURPOSE: The purpose of this systematic review was to evaluate studies in which health information technology was used to improve ostomy care and management. METHODS: Systematic literature review. SEARCH STRATEGIES: The review was performed according to PRISMA Guidelines. Three scientific databases, Scopus, PubMed, and Web of Science, were searched with no time limitation using key words related to information technology and ostomy. The selection of articles and data collection were carried out by 2 reviewers and disagreements were resolved via discussion with a third, independent reviewer. FINDINGS: The initial search of electronic databases retrieved 1679 elements; following removal of duplicate records, title and abstract review, and articles read in full for inclusion/exclusion criteria, 10 articles were included in the review. Analysis of findings from studies included in our review addresses technologies used to care for persons living with an ostomy. Elements were divided into 2 categories: (1) sensor-based wearable technologies, which were mostly used to assess the fecal output and fullness of ostomy pouching system, and (2) computer-based, tablet based, and smartphones platforms, which were used for teaching and learning. The most significant outcomes were increasing patients' knowledge and awareness of ostomy, enhancing patient's participation in self-care processes, and improving self-efficacy levels. IMPLICATIONS FOR PRACTICE: We found limited research regarding the effectiveness of technology-based interventions on the management of ostomy patients. Findings of this systematic review suggest that the application of technologies has created a positive effect on the management of an ostomy, provided opportunities for enhancing self-efficacy, self-care, and self-management. The results of this study can be a basis for designing efficient technology-based systems for the management of ostomy.

Investigating the role of health information technology in the control and management of Systemic Lupus Erythematosus (SLE): a systematic review.

BACKGROUND: Despite the use of health information technology (HIT) for controlling and managing lupus, its effectiveness has not been well studied. The objective of this study was to investigate the role of HIT in controlling and managing lupus. METHODS: We searched Scopus, PubMed, Web of Science, and Embase, using "self-management", "self-care" and "Systemic Lupus Erythematosus" keywords. Two researchers selected relevant papers and extracted data using a data collection form. Disagreements were resolved in consultation with the third and fourth researchers. After extraction, the data were analyzed. RESULTS: Totally, 23 papers met the inclusion criteria. About 75% of the studies used web and telephone-based technologies. Most services provided with health technologies were 'Training' and 'consulting'. The 'lifestyle" and 'Consultation and education' axes were the most widely used HIT services to control and manage lupus. While, 'Better management and control of the disease', 'Increasing knowledge and awareness of people about lupus' and 'Improving behaviors and attitudes toward self-management and self-care' were also the most important outcomes. 'Collectiing patient data and information', 'Providing education and consultation services to patients', 'Measuring patient-reported outcomes', and 'Increasing patients' knowledge and awareness of their disease' were the most important advantages of various technologies. 'Slow internet speed' and 'Challenges and problems related to appearance and usability' and 'Patient concerns about privacy and misuse of their data' were three disadvantages of technologies. CONCLUSION: The findings showed that HIT can improve the management and control of lupus and facilitate self-efficacy, self-care, and self-management in patients. The axes and data elements identified in this study can be the basis for developing and implementing efficient HIT-based systems to improve, control, and manage lupus.

Survey of the patients' perspectives and preferences in adopting telepharmacy versus in-person visits to the pharmacy: a feasibility study during the COVID-19 pandemic.

BACKGROUND: Following the coronavirus disease 2019 (COVID-19) pandemic, the health authorities recommended the implementation of strict social distancing and complete lockdown regulations to reduce disease spread. The pharmacists quickly adopted telemedicine (telepharmacy) as a solution against this crisis, but awareness about this technology is lacking. Therefore, the purpose of this research was to explore the patients' perspectives and preferences regarding telepharmacy instead of traditional in-person visits. METHODS: An electronic questionnaire was designed and sent to 313 patients who were eligible for the study (from March to April 2021). The questionnaire used five-point Likert scales to inquire about motivations for adopting telepharmacy and in-person visits, their perceived advantages and disadvantages, and the declining factors of telepharmacy. Finally, the results were descriptively analyzed using SPSS 22. RESULTS: Of all 313 respondents, a total of 241 (77%) preferred appointments via telepharmacy while 72 (23%) preferred in-person services. There was a significant difference between the selection percentage of telepharmacy and in-person services (chi-square 91.42; p < 0.0001). Preference bout the telepharmacy system versus in-person visits to the pharmacy was associated with factors such as "reducing the incidence of contagious disease" (4.41; ± 0.78), "spending less time receiving pharmaceutical services" (4.24; ± 0.86)), and "traveling a shorter distance for receiving pharmaceutical services" (4.25; ± 0.86). "Reducing costs" (90.87%), "saving time" (89.21%), and "reducing the incidence of contagious disease" (87.13%) were the most important reasons for choosing telepharmacy services. Also, "face-to-face communication with the pharmacist" (25%), "low internet bandwidth" (25%), and "reduction of patients' anxiety and the increase of their peace of mind" (23.61%) were the most important reasons for choosing in-person visits. CONCLUSION: Survey data indicate that most participants are likely to prefer the use of telepharmacy, especially during crises such as the current COVID-19 pandemic. Telepharmacy can be applied as an important means and a crucial service to lessen the load on healthcare organizations and expand drug supply shelters in pharmacies. However, there are still substantial hurdles to overcome in order to successfully implement the telemedicine platform as part of mainstream practice.

Comparing machine learning algorithms for predicting COVID-19 mortality.

BACKGROUND: The coronavirus disease (COVID-19) hospitalized patients are always at risk of death. Machine learning (ML) algorithms can be used as a potential solution for predicting mortality in COVID-19 hospitalized patients. So, our study aimed to compare several ML algorithms to predict the COVID-19 mortality using the patient's data at the first time of admission and choose the best performing algorithm as a predictive tool for decision-making. METHODS: In this study, after feature selection, based on the confirmed predictors, information about 1500 eligible patients (1386 survivors and 144 deaths) obtained from the registry of Ayatollah Taleghani Hospital, Abadan city, Iran, was extracted. Afterwards, several ML algorithms were trained to predict COVID-19 mortality. Finally, to assess the models' performance, the metrics derived from the confusion matrix were calculated. RESULTS: The study participants were 1500 patients; the number of men was found to be higher than that of women (836 vs. 664) and the median age was 57.25 years old (interquartile 18-100). After performing the feature selection, out of 38 features, dyspnea, ICU admission, and oxygen therapy were found as the top three predictors. Smoking, alanine aminotransferase, and platelet count were found to be the three lowest predictors of COVID-19 mortality. Experimental results demonstrated that random forest (RF) had better performance than other ML algorithms with accuracy, sensitivity, precision, specificity, and receiver operating characteristic (ROC) of 95.03%, 90.70%, 94.23%, 95.10%, and 99.02%, respectively. CONCLUSION: It was found that ML enables a reasonable level of accuracy in predicting the COVID-19 mortality. Therefore, ML-based predictive models, particularly the RF algorithm, potentially facilitate identifying the patients who are at high risk of mortality and inform proper interventions by the clinicians.

Designing and Evaluating a Mobile-based Self-care Application for Patients with Gastrointestinal Cancer to Manage Chemotherapy Side Effects.

Background: Controlling and managing the side effects of chemotherapy is one of the most serious challenges that patients with gastrointestinal cancer encounter. A promising technique to overcome these challenges is using informative mobile-based applications. The aim of this study was to design and evaluate a mobile-based application to help patients with gastrointestinal cancer to manage the possible side effects caused by chemotherapy. Methods: This descriptive-applied study was performed in 2 stages. In the first stage, a needs assessment was performed where the opinions of 4 oncologists and 27 patients with gastrointestinal cancer were obtained by use of a researcher-designed questionnaire. In the second stage of the study, based on the identified needs from the first stage, an application prototype was designed and later evaluated. Participants were asked to use the application for 1 week to evaluate the usability of the application. The Questionnaire for User Interaction Satisfaction Version 5.5 was used for evaluation. The results of the study were analyzed using descriptive statistics and SPSS software Version 22. Results: Of the 34 data elements obtained in the first step, 30 gained a mean above 3.75 and were considered in designing the application. The following features were included in the application: demographic data, history, clinical data, managing psychological and psychiatric challenges, lifestyle information, management of side effects, communication possibility, and other application features. Also, the evaluation results showed that the users gave a mean of 7.12 to the application and believed its usability was good. Conclusion: This application and its capabilities can help patients with gastrointestinal cancer undergoing chemotherapy to better perform self-care processes, improve their health status, and reduce the side effects of chemotherapy.

Design and development of a web-based registry for Coronavirus (COVID-19) disease.

Background: The 2019 coronavirus (COVID-19) is a highly contagious disease associated with a high morbidity and mortality worldwide. The accumulation of data through a prospective clinical registry enables public health authorities to make informed decisions based on real evidence obtained from surveillance of COVID-19. This registry is also fundamental to providing robust infrastructure for future research surveys. The purpose of this study was to design a registry and its minimum data set (MDS), as a valid and reliable data source for reporting and benchmarking COVID-19. Methods: This cross sectional and descriptive study provides a template for the required MDS to be included in COVID-19 registry. This was done by an extensive literature review and 2 round Delphi survey to validate the content, which resulted in a web-based registry created by Visual Studio 2019 and a database designed by Structured Query Language (SQL). Results: The MDS of COVID-19 registry was categorized into the administrative part with 3 sections, including 30 data elements, and the clinical part with 4 sections, including 26 data elements. Furthermore, a web-based registry with modular and layered architecture was designed based on final data classes and elements. Conclusion: To the best of our knowledge, COVID-19 registry is the first designed instrument from information management perspectives in Iran and can become a homogenous and reliable infrastructure for collecting data on COVID-19. We hope this approach will facilitate epidemiological surveys and support policymakers to better plan for monitoring patients with COVID-19.

Monitoring of external predisposing factors for Diabetic Foot: A literature review and physicians' perspectives.

Background: Diabetic foot is one of the most important complications of diabetes caused by the existence of some destructive factors in different anatomical locations of feet. Management and monitoring of these factors are very important to decrease or avoid ulcerating lesions of the foot. The purpose of this study is to identify and introduce the predisposing factors and anatomical locations associated with these destructive factors. Methods: First, we conducted a comprehensive review of different databases to identify the factors and associated anatomical locations from the previous studies. Then, we designed a questionnaire and invited physicians and specialists to express their perspectives on these factors and locations. The data were analyzed using SPSS version 23. Frequency, percentage, mean and standard deviation of these variables were calculated. Results: Based on the literature review, four factors, including pressure, moisture and sweat, temperature, and acceleration were identified as factors destructive to the tissues of the diabetic foot and worsen ulcers. The view of specialists approved the results of the literature review. Besides, there was an insignificant difference between the results of the literature review and the specialists' view in terms of anatomical locations that need to be continuously monitored. Conclusion: Monitoring the pressure in heel, first metatarsal, and first metatarsal head; moisture and sweat under the fingers, hallux and heels as well as the temperature at the first metatarsal, first metatarsal head, and the third metatarsal head are important in preventing ulceration, destructing the foot tissue, and accelerating the treatment process.

The most used questionnaires for evaluating the usability of robots and smart wearables: A scoping review.

Background: As the field of robotics and smart wearables continues to advance rapidly, the evaluation of their usability becomes paramount. Researchers may encounter difficulty in finding a suitable questionnaire for evaluating the usability of robotics and smart wearables. Therefore, the aim of this study is to identify the most commonly utilized questionnaires for assessing the usability of robots and smart wearables. Methods: A comprehensive search of databases, including PubMed, Web of Science, and Scopus, was conducted for this scoping review. Two authors performed the selection of articles and data extraction using a 10-field data extraction form. In cases of disagreements, a third author was consulted to reach a consensus. The inclusions were English-language original research articles that utilized validated questionnaires to assess the usability of healthcare robots and smart wearables. The exclusions comprised review articles, non-English publications, studies not focused on usability, those assessing clinical outcomes, articles lacking questionnaire details, and those using non-validated or researcher-made questionnaires. Descriptive statistics methods (frequency and percentage), were employed to analyze the data. Results: A total of 314 articles were obtained, and after eliminating irrelevant and duplicate articles, a final selection of 50 articles was included in this review. A total of 17 questionnaires were identified to evaluate the usability of robots and smart wearables, with 10 questionnaires specifically for wearables and 7 questionnaires for robots. The System Usability Scale (50%) and Post-Study System Usability Questionnaire (19.44%) were the predominant questionnaires utilized to assess the usability of smart wearables. Moreover, the most commonly used questionnaires for evaluating the usability of robots were the System Usability Scale (56.66%), User Experience Questionnaire (16.66%), and Quebec User Evaluation of Satisfaction with Assistive Technology (10%). Conclusion: Commonly employed questionnaires serve as valuable tools in assessing the usability of robots and smart wearables, aiding in the refinement and optimization of these technologies for enhanced user experiences. By incorporating user feedback and insights, designers can strive towards creating more intuitive and effective robotic and wearable solutions.

Evaluating the effects of mobile application-based rehabilitation on improving disability and pain in patients with disputed thoracic outlet syndrome: A randomized controlled trial.

BACKGROUND: Disputed thoracic outlet syndrome (D.TOS) stands as one of the primary global contributors to physical disability, presenting diagnostic and treatment challenges for patients and frequently resulting in prolonged periods of pain and functional impairment. Mobile applications emerge as a promising avenue in aiding patient self-management and rehabilitation for D.TOS. This study aimed to investigate the impact of a certain mobile application-based rehabilitation on pain relief and the improvement of disability in patients experiencing D.TOS. METHODS: Eighty-eight patients diagnosed with D.TOS randomized 1:1 to either the control group (n = 44) or the intervention group (n = 44). Participants in the control group were provided with a brochure containing standard rehabilitation exercise instructions, a written drug prescription from the physician, and guidance on recommended physical activity levels, including home exercises. In contrast, all participants in the intervention group used the mobile application. Disability and pain levels in patients were assessed after six weeks in both groups. RESULT: Both groups improved pain and disability based on the scaled measurements. According to the questionnaire scale, the intervention group showed a considerable decline in disability; however, there was a significant difference in just one question (P < 0.05). Furthermore, the intervention group showed significant improvement in neck pain NRS (p = 0.024) compared to the control. Based on the shoulder and head pain numeric rate scale (NRSs), both groups showed improvement in disability conditions; but there were no significant differences between the groups (p > 0.05). CONCLUSION: Mobile applications are promising tools for alleviating disabilities and pain in patients with musculoskeletal conditions. This study confirmed the potential of mobile technology to enhance active and corrective physical activity, thereby reducing pain in patients with D.TOS. TRIAL REGISTRATION: Iranian Registry of Clinical Trials (IRCT) with the identifier IRCT20141221020380N3 (http://www.irct.ir/).

Prediction of childbearing tendency in women on the verge of marriage using machine learning techniques.

The declining fertility rate and increasing marriage age among girls pose challenges for policymakers, leading to issues such as population decline, higher social and economic costs, and reduced labor productivity. Using machine learning (ML) techniques to predict the desire to have children can offer a promising solution to address these challenges. Therefore, this study aimed to predict the childbearing tendency in women on the verge of marriage using ML techniques. Data from 252 participants (203 expressing a "desire to have children" and 49 indicating "reluctance to have children") in Abadan, and Khorramshahr cities (Khuzestan Province, Iran) was analyzed. Seven ML algorithms, including multilayer perceptron (MLP), support vector machine (SVM), logistic regression (LR), random forest (RF), J48 decision tree, Naive Bayes (NB), and K-nearest neighbors (KNN), were employed. The performance of these algorithms was assessed using metrics derived from the confusion matrix. The RF algorithm showed superior performance, with the highest sensitivity (99.5%), specificity (95.6%), and receiver operating characteristic curve (90.1%) values. Meanwhile, MLP emerged as the top-performing algorithm, showcasing the best overall performance in accuracy (77.75%) and precision (81.8%) compared to other algorithms. Factors such as age of marriage, place of residence, and strength of the family center with the birth of a child were the most effective predictors of a woman's desire to have children. Conversely, the number of daughters, the wife's ethnicity, and the spouse's ownership of assets such as cars and houses were among the least important factors in predicting this desire. ML algorithms exhibit excellent predictive capabilities for childbearing tendencies in women on the verge of marriage, highlighting their remarkable effectiveness. This capacity to offer accurate prognoses holds significant promise for advancing research in this field.

Generative artificial intelligence in healthcare: A scoping review on benefits, challenges and applications.

BACKGROUND: Generative artificial intelligence (GAI) is revolutionizing healthcare with solutions for complex challenges, enhancing diagnosis, treatment, and care through new data and insights. However, its integration raises questions about applications, benefits, and challenges. Our study explores these aspects, offering an overview of GAI's applications and future prospects in healthcare. METHODS: This scoping review searched Web of Science, PubMed, and Scopus . The selection of studies involved screening titles, reviewing abstracts, and examining full texts, adhering to the PRISMA-ScR guidelines throughout the process. RESULTS: From 1406 articles across three databases, 109 met inclusion criteria after screening and deduplication. Nine GAI models were utilized in healthcare, with ChatGPT (n = 102, 74 %), Google Bard (Gemini) (n = 16, 11 %), and Microsoft Bing AI (n = 10, 7 %) being the most frequently employed. A total of 24 different applications of GAI in healthcare were identified, with the most common being "offering insights and information on health conditions through answering questions" (n = 41) and "diagnosis and prediction of diseases" (n = 17). In total, 606 benefits and challenges were identified, which were condensed to 48 benefits and 61 challenges after consolidation. The predominant benefits included "Providing rapid access to information and valuable insights" and "Improving prediction and diagnosis accuracy", while the primary challenges comprised "generating inaccurate or fictional content", "unknown source of information and fake references for texts", and "lower accuracy in answering questions". CONCLUSION: This scoping review identified the applications, benefits, and challenges of GAI in healthcare. This synthesis offers a crucial overview of GAI's potential to revolutionize healthcare, emphasizing the imperative to address its limitations.

Explainable artificial intelligence (XAI) for predicting the need for intubation in methanol-poisoned patients: a study comparing deep and machine learning models.

The need for intubation in methanol-poisoned patients, if not predicted in time, can lead to irreparable complications and even death. Artificial intelligence (AI) techniques like machine learning (ML) and deep learning (DL) greatly aid in accurately predicting intubation needs for methanol-poisoned patients. So, our study aims to assess Explainable Artificial Intelligence (XAI) for predicting intubation necessity in methanol-poisoned patients, comparing deep learning and machine learning models. This study analyzed a dataset of 897 patient records from Loghman Hakim Hospital in Tehran, Iran, encompassing cases of methanol poisoning, including those requiring intubation (202 cases) and those not requiring it (695 cases). Eight established ML (SVM, XGB, DT, RF) and DL (DNN, FNN, LSTM, CNN) models were used. Techniques such as tenfold cross-validation and hyperparameter tuning were applied to prevent overfitting. The study also focused on interpretability through SHAP and LIME methods. Model performance was evaluated based on accuracy, specificity, sensitivity, F1-score, and ROC curve metrics. Among DL models, LSTM showed superior performance in accuracy (94.0%), sensitivity (99.0%), specificity (94.0%), and F1-score (97.0%). CNN led in ROC with 78.0%. For ML models, RF excelled in accuracy (97.0%) and specificity (100%), followed by XGB with sensitivity (99.37%), F1-score (98.27%), and ROC (96.08%). Overall, RF and XGB outperformed other models, with accuracy (97.0%) and specificity (100%) for RF, and sensitivity (99.37%), F1-score (98.27%), and ROC (96.08%) for XGB. ML models surpassed DL models across all metrics, with accuracies from 93.0% to 97.0% for DL and 93.0% to 99.0% for ML. Sensitivities ranged from 98.0% to 99.37% for DL and 93.0% to 99.0% for ML. DL models achieved specificities from 78.0% to 94.0%, while ML models ranged from 93.0% to 100%. F1-scores for DL were between 93.0% and 97.0%, and for ML between 96.0% and 98.27%. DL models scored ROC between 68.0% and 78.0%, while ML models ranged from 84.0% to 96.08%. Key features for predicting intubation necessity include GCS at admission, ICU admission, age, longer folic acid therapy duration, elevated BUN and AST levels, VBG_HCO3 at initial record, and hemodialysis presence. This study as the showcases XAI's effectiveness in predicting intubation necessity in methanol-poisoned patients. ML models, particularly RF and XGB, outperform DL counterparts, underscoring their potential for clinical decision-making.

Medication adherence in patients with mental disorders: A systematic review and meta-analysis of telemedicine interventions.

BACKGROUND AND OBJECTIVE: Telemedicine interventions have emerged as a promising solution to improve medication adherence by providing remote support and monitoring of patients with mental disorders. This study aims to investigate the effectiveness of telemedicine interventions in enhancing medication adherence among patients with mental disorders. METHODS: PubMed, Scopus, and Web of Science were searched systematically. After deleting the double-included studies, two researchers independently selected articles and extracted data using a standardized data collection form. The risk of bias in the included studies was assessed using the Mixed Methods Appraisal Tool. The intervention effects were combined using a random effects model. Standardized mean differences (Hedges's g) between the treatment and control groups were calculated. Heterogeneity variance was estimated using the Q test and I2 statistic. The analysis was performed in Stata version 17.0. RESULTS: Out of the 1088 articles retrieved, nine studies were included in the analysis. Overall, telemedicine interventions demonstrated a statistically significant improvement in medication adherence among patients with mental disorders (Hedges' g = 0.25, 95% confidence interval: 0.12-0.38, p-value: < 0.01). The type of mental disorder was a significant moderator of the heterogeneity between studies (p = 0.022). CONCLUSION: Telemedicine interventions have a positive impact on medication adherence in patients with mental disorders by offering remote support and monitoring. Integrating telemedicine into mental healthcare can enhance overall adherence rates, leading to improved management of mental disorders.

Examining Outcomes and Challenges of Telepsychiatry in Australian Elderly: A Scoping Review.

Methods: To find relevant articles, we searched PubMed, Scopus, and Web of Science databases. We used a data extraction form to gather information from primary studies. Two researchers followed inclusion and exclusion criteria to select studies and extract data. Disagreements were resolved through discussion with all researchers. Studies needed to be in English, about telepsychiatry for Australian seniors, and use any technology type (synchronous, asynchronous, or both). We excluded nontelepsychiatry articles, books, book chapters, conference abstracts, and editor letters. Results: Telepsychiatry was effectively employed to manage depression, anxiety, delirium, and cognitive impairments. Among these four disorders, telepsychiatry was mostly used for depression. Videoconference and telephone were mostly used to provide telepsychiatry services. Most telepsychiatry services for Australian seniors included "patient education on disorder control and management," "creating continuous interaction between the patient and the therapist," and "remote patients' assessment." "Reductions in symptoms of disorders," "improving patients' satisfaction with telepsychiatry," and "cost-effectiveness of telepsychiatry" were the most important positive outcomes of using telepsychiatry. We also identified four challenges in using telepsychiatry for elderly individuals in Australia. Conclusions: This study is the first scoping review in Australia and provides valuable insight into telepsychiatry for elderly individuals.