Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

Background: Telemedicine involves medical practice and information and communications technology. It has been proven to be very effective for remote health care, especially in areas with poor provision of health facilities. However, implementation of these technologies is often hampered by various issues. Among these, ethical and legal concerns are some of the more complex and diverse ones. In this study, an analysis of scientific literature was carried out to identify the ethical and legal challenges of telemedicine. Materials and Methods: English literature, published between 2010 and 2019, was searched on PubMed, Scopus, and Web of Science by using keywords, including “Telemedicine,” “Ethics,” “Malpractice,” “Telemedicine and Ethics,” “Telemedicine and Informed consent,” and “telemedicine and malpractice.” Different types of articles were analyzed, including research articles, review articles, and qualitative studies. The abstracts were evaluated according to the selection criteria, using the Newcastle–Ottawa Scale criteria, and the final analysis led to the inclusion of 22 articles. Discussion: From the aforementioned sample, we analyzed elements that may be indicative of the efficacy of telemedicine in an adequate time frame. Ethical aspects such as informed consent, protection data, confidentiality, physician's malpractice, and liability and telemedicine regulations were considered. Conclusions: Our objective was to highlight the current status and identify what still needs to be implemented in telemedicine with respect to ethical and legal standards. Gaps emerged between current legislation, legislators, service providers, different medical services, and most importantly patient interaction with his/her data and the use of that data.

During the recent COVID-19 pandemic, healthcare providers have been encouraged to increase their use of telemedicine and to adopt telemedicine platforms for the majority of their clients who have chronic illnesses. Due to the outbreak itself, almost all countries worldwide were placed under emergency lockdowns. In this paper, we reviewed the literature regarding the use of telemedicine during the COVID-19 pandemic. Consequentially, we identified the adoption of telemedicine in various countries worldwide and evaluated their future steps in order to increase the adoption of e-health technologies. As a result of COVID-19, the e-health agenda, especially telemedicine, has been accelerated in several countries. COVID-19 is affecting individuals’ daily lives and has created major difficulties in the management of healthcare facilities for both infected and non-infected patients. A large portion of the rapid increase in the use of telemedicine can be attributed to evidence from previous pandemics as well as progress made by the field in response to COVID-19, especially in industrialized countries. A lack of effective treatment, large numbers of unvaccinated individuals, as well as social distancing and lockdown measures suggest telemedicine is the safest and most appropriate way of working with patients and doctors. In spite of this willingness, a large number of barriers need to be overcome in order for the telemedicine system to function properly and effectively throughout countries. In order for telemedicine to be sustainable and beneficial beyond the pandemic, several technical, educational, infrastructure, legal, and economic issues must be addressed and solved.

Objectives: Merchant ships represent a peculiar working environment with several challenges and risks. The specific situation on board of ships may affect the mental health of seafarers more remarkably than ashore workers. Content: A systematic review of the literature has been carried out to identify the main causes of mood disorders among seafarers and the impact that these disorders have on their health. This review has analyzed the scientific literature published between January 2006 and December 2021 using the search engines PubMed, Web of Science (WoS) and Cumulative Index to Nursing and Allied Health Literature (CINAHL). Summary: Social isolation, distance from families, fatigue, stress and long work shifts represent the main causes of mood disorders among seafarers. Outlook: Strategies aimed at improving conditions of cohabitation on board, and a greater consideration of these problems are key for improving the mental health of workers at sea.

Background: The progressive aging of populations, primarily in the industrialized western world, is accompanied by the increased incidence of several non-transmittable diseases, including neurodegenerative diseases and adult-onset dementia disorders. To stimulate adequate interventions, including treatment and preventive measures, an early, accurate diagnosis is necessary. Conventional magnetic resonance imaging (MRI) represents a technique quite common for the diagnosis of neurological disorders. Increasing evidence indicates that the association of artificial intelligence (AI) approaches with MRI is particularly useful for improving the diagnostic accuracy of different dementia types. Objectives: In this work, we have systematically reviewed the characteristics of AI algorithms in the early detection of adult-onset dementia disorders, and also discussed its performance metrics. Methods: A document search was conducted with three databases, namely PubMed (Medline), Web of Science, and Scopus. The search was limited to the articles published after 2006 and in English only. The screening of the articles was performed using quality criteria based on the Newcastle–Ottawa Scale (NOS) rating. Only papers with an NOS score ≥ 7 were considered for further review. Results: The document search produced a count of 1876 articles and, because of duplication, 1195 papers were not considered. Multiple screenings were performed to assess quality criteria, which yielded 29 studies. All the selected articles were further grouped based on different attributes, including study type, type of AI model used in the identification of dementia, performance metrics, and data type. Conclusions: The most common adult-onset dementia disorders occurring were Alzheimer’s disease and vascular dementia. AI techniques associated with MRI resulted in increased diagnostic accuracy ranging from 73.3% to 99%. These findings suggest that AI should be associated with conventional MRI techniques to obtain a precise and early diagnosis of dementia disorders occurring in old age.