Despite the benefits of using virtual reality (VR) in medical education and treatment, some challenges and limitations result in the uselessness or misuse of this technology. Therefore, recognizing potential challenges related to VR might be helpful in the strategic decision-making process to implement and develop this technology in the healthcare field. Accordingly, our review aimed to determine the challenges associated with the application of VR in the field of medical education and treatment. We searched Science Direct, Google Scholar, and PubMed databases for relevant papers using a defined search query. We restricted the search to articles in English or Persian language published by the end of 2018. The main challenges of developing and using VR with educational and therapeutic objectives are categorized as general and specific. General challenges include reduced face-to-face communications, education, cost challenges, users’ attitudes, and specific challenges such as designing, safety considerations, VR side effects, evaluation, and validation of VR applications. Challenges related to VR will have different effects, thus identifying each of them helps to determine the solutions for each challenge. Also, it is suggested to develop and update laws, standards, and protocols, which play an important role in increasing the effective application of VR at the national level.

Background. Leukemia is fatal cancer in both children and adults and is divided into acute and chronic. Acute lymphoblastic leukemia (ALL) is a subtype of this cancer. Early diagnosis of this disease can have a significant impact on the treatment of this disease. Computational intelligence‐oriented techniques can be used to help physicians identify and classify ALL rapidly. Materials and Method. In this study, the utilized dataset was collected from a CodaLab competition to classify leukemic cells from normal cells in microscopic images. Two famous deep learning networks, including residual neural network (ResNet‐50) and VGG‐16 were employed. These two networks are already trained by our assigned parameters, meaning we did not use the stored weights; we adjusted the weights and learning parameters too. Also, a convolutional network with ten convolutional layers and 2∗2 max‐pooling layers—with strides 2—was proposed, and six common machine learning techniques were developed to classify acute lymphoblastic leukemia into two classes. Results. The validation accuracies (the mean accuracy of training and test networks for 100 training cycles) of the ResNet‐50, VGG‐16, and the proposed convolutional network were found to be 81.63%, 84.62%, and 82.10%, respectively. Among applied machine learning methods, the lowest obtained accuracy was related to multilayer perceptron (27.33%) and highest for random forest (81.72%). Conclusion. This study showed that the proposed convolutional neural network has optimal accuracy in the diagnosis of ALL. By comparing various convolutional neural networks and machine learning methods in diagnosing this disease, the convolutional neural network achieved good performance and optimal execution time without latency. This proposed network is less complex than the two pretrained networks and can be employed by pathologists and physicians in clinical systems for leukemia diagnosis.

In most countries chronic diseases lead to high health care costs and reduced productivity of people in society. The best way to reduce costs of health sector and increase the empowerment of people is prevention of chronic diseases and appropriate health activities management through monitoring of patients. To enjoy the full benefits of E-health, making use of methods and modern technologies is very important. this literature review articles were searched with keywords like Patient monitoring, Mobile Health, and Chronic Disease in Science Direct, Google Scholar and Pub Med databases without regard to the year of publications. Applying remote medical diagnosis and monitoring system based on mobile health systems can help significantly to reduce health care costs, correct performance management particularly in chronic disease management. Also some challenges are in patient monitoring in general and specific aspects like threats to confidentiality and privacy, technology acceptance in general and lack of system interoperability with electronic health records and other IT tools, decrease in face to face communication between doctor and patient, sudden interruptions of telecommunication networks, and device and sensor type in specific aspect. It is obvious identifying the opportunities and challenges of mobile technology and reducing barriers, strengthening the positive points will have a significant role in the appropriate planning and promoting the achievements of the health care systems based on mobile and helps to design a roadmap for improvement of mobile health.

Nanoparticles have been used extensively in different scientific fields. Due to the possible destructive effects of nanoparticles on the environment or the biological systems, their toxicity evaluation is a crucial phase for studying nanomaterial safety. In the meantime, experimental approaches for toxicity assessment of various nanoparticles are expensive and time-consuming. Thus, an alternative technique, such as artificial intelligence (AI), could be valuable for predicting nanoparticle toxicity. Therefore, in this review, the AI tools were investigated for the toxicity assessment of nanomaterials. To this end, a systematic search was performed on PubMed, Web of Science, and Scopus databases. Articles were included or excluded based on pre-defined inclusion and exclusion criteria, and duplicate studies were excluded. Finally, twenty-six studies were included. The majority of the studies were conducted on metal oxide and metallic nanoparticles. In addition, Random Forest (RF) and Support Vector Machine (SVM) had the most frequency in the included studies. Most of the models demonstrated acceptable performance. Overall, AI could provide a robust, fast, and low-cost tool for the evaluation of nanoparticle toxicity.

Cancer Care Management through a Mobile Phone Health Approach: Key Considerations Cancer care;management;mobile health;key considerations; Greater use of mobile phone devices seems inevitable because the health industry and cancer care are facing challenges such as resource constraints, rising care costs, the need for immediate access to healthcare data of types such as audio video texts for early detection and treatment of patients and increasing remote aids in telemedicine. Physicians, in order to study the causes of cancer, detect cancer earlier, act in prevention measures, determine the effectiveness of treatment and specify the reasons for the treatment ineffectiveness, need to access accurate, comprehensive and timely cancer data. Mobile devices provide opportunities and can play an important role in consulting, diagnosis, treatment, and quick access to health information. There easy carriage make them perfect tools for healthcare providers in cancer care management. Key factors in cancer care management systems through a mobile phone health approach must be considered such as human resources, confidentiality and privacy, legal and ethical issues, appropriate ICT and provider infrastructure and costs in general aspects and interoperability, human relationships, types of mobile devices and telecommunication related points in specific aspects. The successful implementation of mobile-based systems in cancer care management will constantly face many challenges. Hence, in applying mobile cancer care, involvement of users and considering their needs in all phases of project, providing adequate bandwidth, preparation of standard tools that provide maximum mobility and flexibility for users, decreasing obstacles to interrupt network communications, and using suitable communication protocols are essential. It is obvious that identifying and reducing barriers and strengthening the positive points will have a significant role in appropriate planning and promoting the achievements of mobile cancer care systems. The aim of this article is to explain key points which should be considered in designing appropriate mobile health systems in cancer care as an approach for improving cancer care management.