Background: Musculoskeletal disorders (MSDs) involve muscles, nerves, tendons, joints, cartilage, and spinal discs. These conditions can be triggered by both the work environment and the type of work performed, factors that, in some cases, can also exacerbate pre-existing conditions. This systematic review aims to provide an overview of the impact that different work-related activities have on the musculoskeletal system. Methods: A global search of publications was conducted using the following international bibliographic web databases: PubMed and Web of Science. The search strategies combined terms for musculoskeletal disorders and workers. In addition, a meta-analysis was conducted to estimate the prevalence of MSDs within the healthcare sector. Results: A total of 10,805 non-duplicated articles were identified, and finally, 32 studies were reviewed in this article. Once the literature search was completed, occupational figures were categorized into healthcare, farming, industrial, and computer sectors. In the healthcare sector, the prevalence estimate for degenerative diseases of the lumbar spine was 21% (497 out of 2547 physicians and dentists) (95% CI, 17-26%), while for osteoarthritis of the hand, it was 37% (382 out of 1013 dentists) (95% CI, 23-51%). Conclusions: Musculoskeletal disorders significantly impair workers\' quality of life, especially in healthcare sector. These conditions are also associated with high costs for employers, such as absenteeism, lost productivity, and increased costs for healthcare, disability, and workers\' compensation.

Advancements in diagnostic methods and surgical techniques for keratoconus (KC) have increased non-invasive treatment options. Successful surgical planning for KC involves a combination of clinical science, empirical evidence, and surgical expertise. Assessment of disease progression is crucial, and halting the progression should be the focus if it is progressive. While surgeons used to rely on experience alone to decide the surgical method, comparing the network of primary factors, such as visual acuity, across studies can help them choose the most appropriate treatments for each patient and achieve optimal outcomes. Meticulous tabulation methods facilitate interpretation, highlighting the importance of selecting the correct surgical and rehabilitation approach based on each patient\'s condition and stage of the disease. We detail the outcomes of a comprehensive network meta-analysis comparing the effectiveness of various combined therapeutic refractive treatments for KC at identical stages of the disease, spanning 4 distinct follow-up intervals. Additionally, the comprehensive analysis suggests that for corneas with optimal best corrected visual acuity (BCVA) preoperatively (classified as regular), combining phakic intraocular lenses with intracorneal ring segments (ICRS) and corneal cross-linking (CXL) could offer the best therapeutic approach provided the disease stage does not exceed stage 3. For irregular corneas, although initial follow-ups show a significant difference in BCVA with surface ablation, longer-term follow-ups recommend combining surface ablation with ICRS and CXL, especially at higher stages.

UNASSIGNED: New developments in artificial intelligence, particularly with promising results in early detection and management of keratoconus, have favorably altered the natural history of the disease over the last few decades. Features of artificial intelligence in different machine such as anterior segment optical coherence tomography, and femtosecond laser technique have improved safety, precision, effectiveness, and predictability of treatment modalities of keratoconus (from contact lenses to keratoplasty techniques). These options ingrained in artificial intelligence are already underway and allow ophthalmologist to approach disease in the most non-invasive way.
UNASSIGNED: This study comprehensively describes all of the treatment modalities of keratoconus considering machine learning strategies.
UNASSIGNED: A multidimensional comprehensive systematic narrative review.
UNASSIGNED: A comprehensive search was done in the five main electronic databases (PubMed, Scopus, Web of Science, Embase, and Cochrane), without language and time or type of study restrictions. Afterward, eligible articles were selected by screening the titles and abstracts based on main mesh keywords. For potentially eligible articles, the full text was also reviewed.
UNASSIGNED: Artificial intelligence demonstrates promise in keratoconus diagnosis and clinical management, spanning early detection (especially in subclinical cases), preoperative screening, postoperative ectasia prediction after keratorefractive surgery, and guiding surgical decisions. The majority of studies employed a solitary machine learning algorithm, whereas minor studies assessed multiple algorithms that evaluated the association of various keratoconus staging and management strategies. Last but not least, AI has proven effective in guiding the implantation of intracorneal ring segments in keratoconus corneas and predicting surgical outcomes.
UNASSIGNED: The efficient and widespread clinical translation of machine learning models in keratoconus management is a crucial goal of potential future approaches to better visual performance in keratoconus patients.
UNASSIGNED: The article has been registered through PROSPERO, an international database of prospectively registered systematic reviews, with the ID: CRD42022319338.
Keratoconus: from fundamentals to future Artificial intelligence has changed how we treat the eye disease keratoconus in recent years. This study examines the many keratoconus therapies available, including surgery and contact lens wear, and how artificial intelligence can improve the safety and accuracy of these procedures. We combed through numerous papers to locate this data. To achieve the best outcomes, several parameters and methods should be evaluated. According to the study, some elements from eye scans are more useful than others. The idea behind using artificial intelligence is to help patients see better and treat keratoconus more effectively.

The remarkable recent advances in managing keratoconus, the most common corneal ectasia, encouraged researchers to conduct further studies on the disease. Despite the abundance of information about keratoconus, debates persist regarding the detection of mild cases. Early detection plays a crucial role in facilitating less invasive treatments. This review encompasses corneal data ranging from the basic sciences to the application of artificial intelligence in keratoconus patients. Diagnostic systems utilize automated decision trees, support vector machines, and various types of neural networks, incorporating input from various corneal imaging equipment. Although the integration of artificial intelligence techniques into corneal imaging devices may take time, their popularity in clinical practice is increasing. Most of the studies reviewed herein demonstrate a high discriminatory power between normal and keratoconus cases, with a relatively lower discriminatory power for subclinical keratoconus.

Information and communication technologies (ICTs) have become more widely used in the past years to help people with autism spectrum conditions (ASC). Serious games embedded into computers or tablets, as well as social robots, are the most employed ICT-related tools that are appealing to and appropriate for autistic children. The goal of ICT applications is to enhance behavioral abnormalities associated with ASC while also creating an interactive link between one person and one computer. Comparatively, to human-based therapy, ICT tools aid to inspire autistic children by providing predictability and regularity of tasks. Regaining social skills is the primary behavioral goal for which ICT tools have been designed and implemented. In the past several years, many studies have been created to show how effective it is at improving targeted behaviors. However, only a small number of researchers have used an RCT approach to evaluate its effectiveness. In this systematic review, we only included RCT studies where ICT technologies were used to help children with ASC in improving their social skills. Only 14 RCT studies satisfied the criteria and 12 described significant improvements, showing how the use of technology in educational contexts produced better improvement in developing several social skill facets with respect to the traditional face-to-face approach. Some studies used interventions and outcome measures focused on the core ASC symptoms, but many others addressed neurocognitive functions directly, like social cognition or emotional regulation, while other more general functions such as language or adaptive behaviors. We propose a classification based on processes and outcome measures to foster future research in this specific area of research. The behavioral intervention mediated by technological tools such as computer-based, tablet, and social robotics, undoubtedly provides a comfortable environment that promotes constant learning for people with ASC. Evidence provided in this review highlights the translational potential of this field of study in primary care practice and educational settings.

UNASSIGNED: Artificial Inteligence (AI) application in emergency medicine is subject to ethical and legal inconsistencies. The purposes of this study were to map the extent of AI applications in emergency medicine, to identify ethical issues related to the use of AI, and to propose an ethical framework for its use.
UNASSIGNED: A comprehensive literature collection was compiled through electronic databases/internet search engines (PubMed, Web of Science Platform, MEDLINE, Scopus, Google Scholar/Academia, and ERIC) and reference lists. We considered studies published between 1 January 2014 and 6 October 2022. Articles that did not self-classify as studies of an AI intervention, those that were not relevant to Emergency Departments (EDs), and articles that did not report outcomes or evaluations were excluded. Descriptive and thematic analyses of data extracted from the included articles were conducted.
UNASSIGNED: A total of 137 out of the 2175 citations in the original database were eligible for full-text evaluation. Of these articles, 47 were included in the scoping review and considered for theme extraction. This review covers seven main areas of AI techniques in emergency medicine: Machine Learning (ML) Algorithms (10.64%), prehospital emergency management (12.76%), triage, patient acuity and disposition of patients (19.15%), disease and condition prediction (23.40%), emergency department management (17.03%), the future impact of AI on Emergency Medical Services (EMS) (8.51%), and ethical issues (8.51%).
UNASSIGNED: There has been a rapid increase in AI research in emergency medicine in recent years. Several studies have demonstrated the potential of AI in diverse contexts, particularly when improving patient outcomes through predictive modelling. According to the synthesis of studies in our review, AI-based decision-making lacks transparency. This feature makes AI decision-making opaque.

To identify technologies used in vaccine safety communication and evaluate their impact on vaccination intention, uptake, knowledge, attitude, and perceptions of consumers.
We searched 6 electronic databases to identify randomised controlled trials assessing the impact of using technology in vaccine safety communication. The Cochrane Collaboration\'s tool for assessing risk of bias was used to evaluate each study.
We included 22 studies involving 27,109 participants from 8 countries; 15 studies assessed the use of videos and 7 examined innovative technologies. Using videos significantly improved knowledge (n = 3) and participant engagement (n = 2) compared to printed material. Among the innovative technologies, the use of virtual reality, and smartphone applications incorporating social networking or gamification significantly increased vaccination knowledge, confidence, and engagement. The studies showed that narrative messaging increased perceived disease severity (n = 2) and vaccination intention (n = 2).
While the use of innovative technologies is increasing, videos currently remain the most popular technology for vaccine safety communication. Communication technology, particularly with narrative messaging, improves patient engagement and comprehension.
Health authorities should increase focus on using videos and smartphone applications for vaccine safety communication. Collaboration among stakeholders is essential to develop guidelines on effective message content to complement the technology.

This study explored the use of eLearning in teacher education during COVID-19 in Africa. The literature review was an important source of information for this study and employed a historical design. From the review, the findings indicate that eLearning accommodates all people regardless of their geographical background and enhances learner-centeredness and self-study opportunities. Findings also indicate that it enhances access to updated content for students and is cost-effective as opposed to conventional methods that need the presence of the teacher, buildings, teaching and learning materials, and physical libraries. However, the findings indicate that eLearning faces the challenges such as health-related problems because of excessive use of computers for a long time which may result in low vision. Findings also indicate the challenge of the digital divide and a lack of stable Internet services and connectivity as they are rarely accessed in many teacher colleges and universities including, a lack of availability of computers for students in African countries. Further, the findings indicate the other challenge of eLearning based on its vulnerability to online attacks on the websites that may lead to questioning the quality of education provided through the eLearning model. The key argument in this study is that eLearning will remain an unavoidable option for the provision of teacher education in the world and African countries in particular at the threshold of a global pandemic similar to COVID-19 regardless of the risks involved. African Governments need to invest in digital learning by strengthening Internet connectivity and improving the Information, Communication, and Technology (ICT) infrastructure, i.e., the provision of computers and Televisions (TVs) to educational institutions to sustain students\' learning during pandemics similar to COVID-19 and other unpredictable situations.

UNASSIGNED: The challenge of finding autistic children, and finding them early enough to make a difference for them and their families, becomes all the greater in parts of the world where human and material resources are in short supply. Poverty of resources delays interventions, translating into a poverty of outcomes. Digital tools carry potential to lessen this delay because they can be administered by non-specialists in children\'s homes, schools or other everyday environments, they can measure a wide range of autistic behaviours objectively and they can automate analysis without requiring an expert in computers or statistics. This literature review aimed to identify and describe digital tools for screening children who may be at risk for autism. These tools are predominantly at the \'proof-of-concept\' stage. Both portable (laptops, mobile phones, smart toys) and fixed (desktop computers, virtual-reality platforms) technologies are used to present computerised games, or to record children\'s behaviours or speech. Computerised analysis of children\'s interactions with these technologies differentiates children with and without autism, with promising results. Tasks assessing social responses and hand and body movements are the most reliable in distinguishing autistic from typically developing children. Such digital tools hold immense potential for early identification of autism spectrum disorder risk at a large scale. Next steps should be to further validate these tools and to evaluate their applicability in a variety of settings. Crucially, stakeholders from underserved communities globally must be involved in this research, lest it fail to capture the issues that these stakeholders are facing.

BACKGROUND: Interactive conversational agents, also known as \"chatbots,\" are computer programs that use natural language processing to engage in conversations with humans to provide or collect information. Although the literature on the development and use of chatbots for health interventions is growing, important knowledge gaps remain, such as identifying design aspects relevant to health care and functions to offer transparency in decision-making automation.
OBJECTIVE: This paper presents the protocol for a scoping review that aims to identify and categorize the interactive conversational agents currently used in health care.
METHODS: A mixed methods systematic scoping review will be conducted according to the Arksey and O\'Malley framework and the guidance of Peters et al for systematic scoping reviews. A specific search strategy will be formulated for 5 of the most relevant databases to identify studies published in the last 20 years. Two reviewers will independently apply the inclusion criteria using the full texts and extract data. We will use structured narrative summaries of main themes to present a portrait of the current scope of available interactive conversational agents targeting health promotion, prevention, and care. We will also summarize the differences and similarities between these conversational agents.
RESULTS: The search strategy and screening steps were completed in March 2022. Data extraction and analysis started in May 2022, and the results are expected to be published in October 2022.
CONCLUSIONS: This fundamental knowledge will be useful for the development of interactive conversational agents adapted to specific groups in vulnerable situations in health care and community settings.
UNASSIGNED: DERR1-10.2196/40265.