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Background: Pain is a significant symptom in cancer patients that is frequently not effectively treated, and managing it is seen as a crucial aspect of caring for these patients. This severe pain frequently causes a significant disturbance in their quality of life. At present, there are different challenges in utilizing a range of pharmacological and nonpharmacological treatments for managing pain in cancer patients. Recent technological advancements, particularly in artificial intelligence, have improved the management of pain in cancer patients. Artificial intelligence and its algorithms offer potential solutions for pain relief in cancer patients with reduced side effects. Study Design: The current review aimed to assess the validity of studies on using artificial intelligence in pain management for cancer patients. Four databases have been used to review all published studies from the start of 2023: PubMed, Scopus, Web of Science, and Google Scholar. The search mechanism for articles was mainly using valid and mesh‐based keywords, asking experts, and reviewing the literature and including “Pain,” “Pain management,” “Cancer,” and “Artificial intelligence.” During the initial search, a total of 450 articles were found, and after considering the inclusion and exclusion criteria and reviewing the abstract and content of the articles, 15 articles were finally included in the study. Results: AI‐based solutions can provide individual pain relief plans. When AI analyzes large patient data such as physiological signals, responses to treatment, and symptoms of patients who have been diagnosed with pain, it is possible to accurately adjust therapeutic measures. Conclusions: AI enables healthcare providers to offer timely care and assistance to cancer patients through remote monitoring and telehealth services, even when they are not physically present. Despite the presence of hurdles such as ensuring ethical AI practices and protecting data privacy, the integration of AI in oncology pain management brings optimism for the future.

Vitiligo, a chronic autoimmune disorder characterized by the presence of depigmented skin patches, remains a therapeutic challenge due to its multifactorial pathogenesis and the absence of highly effective treatment options. Although the exact etiology of vitiligo is not fully understood, factors such as genetic factors, oxidative stress, autoimmunity, and inflammation are implicated in the destruction of melanocytes. Current therapeutic strategies primarily focus on modulating immune responses and alleviating oxidative stress. Conventional treatments, including topical corticosteroids, phototherapy, and immunosuppressive agents, often exhibit limited efficacy and are associated with significant side effects, limiting their long-term application. In recent years, nanotechnology has emerged as a transformative approach in drug delivery systems, offering precise targeting, enhanced drug bioavailability, and minimized systemic toxicity. Nanocarrier-based systems especially lipid-based nanoparticles (LNPs) effectively address critical barriers in vitiligo treatment, such as poor drug solubility, rapid degradation, and inadequate skin penetration. Moreover, controlled drug release mechanisms offered by LNPs ensure sustained therapeutic drug levels at the target site, improving efficacy and reducing the frequency of administration. This review provides an overview of vitiligo, its pathogenesis, and the limitations of conventional treatments while highlighting recent advancements in LNPs-based drug delivery systems as a promising strategy for the effective management of vitiligo.

Background and Aim Internet addiction can cause anxiety and depression. Exams and living far from home can cause anxiety as well. This research aimed to explore the correlation between Internet addiction, exam-related anxiety, and adult separation anxiety among students. Methods This study used a correlational descriptive method to select a varied group of 258 students from Shahroud University of Medical Sciences by using a multistage sampling technique. Several university majors were selected by using a random selection process. The demographic form, Internet Addiction Test (IAT), Frieden Test Anxiety Scale (FTA), and Adult Separation Anxiety (ASA) were the data collection tools. Results The average Internet addiction score was 37.34 ± 10.18, demonstrating a positive correlation with test anxiety (r = 0.28, P < .000). The average adult separation anxiety score was 35/44 ± 18/24, which showed a significant positive correlation with Internet addiction (r = 0.37, P < .000). Internet addiction was also related to the amount of Internet usage, self-phone usage, being native or not, and studying only the night before the exam instead of studying throughout the semester. Conclusion Internet addiction can be related to test anxiety in students. Moreover, students who experience adult separation anxiety due to living away from familiar situations and people can experience Internet addiction more.

Introduction:Infertility and pregnancy complications are among the unpleasant experiences observed in societies, and their prevalence is not the same in different societies. According to the WHO 2023 report, about 17.5% of the adult population, approximately 1 in 6 worldwide, experience infertility. Screening programs aimed at reducing infertility rates have been implemented, and their effects have been observed in various communities. However, due to existing challenges, not everyone has sufficient access to necessary treatments and screenings. Given the widespread role of artificial intelligence (AI) in modern societies, this study was undertaken to collate and review previous articles on infertility, focusing on screening, diagnosis, and treatment. Search Strategy: A review was conducted independently by two individuals based on PICO criteria and aligned with the research aim using Google Scholar, PubMed, Web of Science, SID, and Magiran search engines. The time limitation was set between 2018 and 2024, using the MESH keywords \"Screening\", \"Treatment\", \"Infertility\", and \"Artificial Intelligence.\" According to the entry and exit criteria in the Prisma checklist, eight articles were selected from 43 primary articles. Results: Studies have demonstrated that AI can provide personalized risk assessment, enable access to infertility specialists through telemedicine, and accurately distinguish viable blastocysts. AI algorithms can predict risks such as preeclampsia, premature birth, mortality, birth weight, miscarriage, and postpartum depression before pregnancy. Additionally, AI algorithms can non-invasively determine sperm chromosomal abnormalities with 70% accuracy, enhancing the quality of medical services and reducing human errors. AI can improve infertility care by detecting fetal defects, assisting in procedures like accurately tracking ovulation cycles, predicting treatment outcomes, and optimizing treatment processes through data analysis. Conclusion and Discussion: AI holds significant potential in addressing infertility. Ethical considerations, infrastructure development, data security, and interdisciplinary cooperation are important for the ethical use of AI in infertility treatment. Considering the vast dimensions of AI, more research is recommended for its greater use in medicine and infertility.

Introduction:Diabetes is a common chronic metabolic disease that poses a significant global healthcare challenge. The International Diabetes Federation projects reported that 693 million people will experience diabetes by 2045. Given the serious complications and mortality associated with diabetes, it is essential to educate patients and promote self-care pratices. Mobile health technologies (mHealth) have become important tools in managing diabetes and supporting self-care. This study aimed to review how mHealth can assist diabetic patients in managing their self-care more effectively. Search Strategy:We utilized PICO criteria to search various databases, including PubMed, Web of Science, Medline, Scopus, SID, and Google Scholar using the keywords \"Mobile Health,\" \"Self-Care,\" and \"Diabetes\" from 2015 to 2023. Two operators independently conducted searches using Boolean operators. After screening and conducting a quality appraisal, 128 articles were identified, of which 11 met the inclusion criteria. Results: The results suggested that integrating AI-based mHealths into diabetes management programs has broadened their functionalities beyond monitoring blood glucose levels and HbA1c. These advanced software solutions have shown potential in promoting physical activity, reducing sedentary behavior, supporting short-term weight loss, assisting with insulin dose adjustments, educating users about diabetes complications, and facilitating data sharing with healthcare professionals for remote monitoring and care. One significant benefit of utilizing mHealths is their accessibility, with many programs being offered at no cost or requiring only a fixed or minimal subscription fee. Studies have indicated high adoption rates of mobile health interventions in underserved areas with limited access to healthcare providers and services. However, challenges and limitations linked to the use of mHealths have been recognized. These include the need for extensive data input, concerns about the security and privacy of personal information, potential erosion of patient trust, as well as issues regarding the accuracy and reliability of health information obtained through these platforms. Conclusion and Discussion: As artificial intelligence (AI) continues to gain traction in healthcare, it is essential to educate providers on the operation of these tools. Emphasizing distance education for technological products can significantly reduce hospital costs. Expanding mobile health initiatives for primary prevention can help mitigate complications associated with diabetes. Although the use of AI remains limited, aligning research policies with technological advancements and fostering interdisciplinary health support is crucial.

Introduction:Approximately 0.4 million people are diagnosed with systemic lupus erythematosus (SLE) yearly. SLE is a chronic autoimmune disease characterized by the immune system attacking healthy tissues and organs. Early SLE diagnosis is crucial as it allows for the prompt initiation of appropriate treatment, which can help prevent disease progression and minimize organ damage. Artificial intelligence (AI) approaches have emerged as promising tools for studying SLE. AI algorithms have analyzed SLE patients' omics data (massive biomolecule datasets) to improve early diagnosis. This review systematically examines practical AI algorithms for omics data analysis and the early diagnosis of SLE. Search Study: The study was conducted based on the PICO criteria and aligned with the research objective, adhering to the PRISMA checklist. This systematic review included a comprehensive search from 2019 to March 2024 across the PubMed, SCOPUS, Web of Science, SID, and Magiran databases, as well as the Google Scholar search engine. The search utilized MESH keywords, including \"Diagnosis\", \"Lupus Erythematosus”, Systemic\", \"Artificial intelligence\", \"multiomics\", \"Genomics\", \"Proteomics\", and \"Metabolomics\". Subsequently, two independent researchers screened the retrieved articles based on inclusion criteria. Results: A total of 94 articles were identified through the initial search. After screening titles and abstracts, the number of articles was reduced to 15. Finally, considering the inclusion and exclusion criteria and after reviewing the complete text, four articles were included in this study. Studies have shown that machine learning technology, which is one of the AI technologies, has been able to analyze a vast amount of omics data by using special techniques such as Uniform Manifold Approximation and Projection (UMAP), Recursive Feature Elimination (RFE), Least Absolute Shrinkage and Selection Operator (LASSO), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM), which helps in the prediction and early diagnosis of SLE. The UMAP technique, with its mechanism of dimensionality reduction, has contributed to a more accurate diagnosis and treatment of SLE by identifying important patterns. Additionally, the RFE and LASSO techniques have provided more accurate predictions of the probability of individuals developing SLE by selecting the most important data from the omics datasets. Furthermore, the XGBoost and SVM techniques have played a significant role in SLE diagnosis by analyzing various data sources and identifying disease-related patterns. Conclusion and Discussion: AI-powered SLE diagnosis using omics data holds promise for improving the accuracy and timeliness of SLE diagnosis. However, further research is needed to validate these approaches and establish their clinical utility due to the limitations of the studies conducted in this field.