External Validation of the Discriminative Validity of the ReSVinet Score and Development of Simplified ReSVinet Scores in Secondary Care.

Background: There is no consensus on how to best quantify disease severity in infants with respiratory syncytial virus (RSV) and/or bronchiolitis; this lack of a sufficiently validated score complicates the provision of clinical care and, the evaluation of trials of therapeutics and vaccines. The ReSVinet score appears to be one of the most promising; however, it is too time consuming to be incorporated into routine clinical care. We aimed to develop and externally validate simplified versions of this score.

Artificial Intelligence-Enabled Social Media Analysis for Pharmacovigilance of COVID-19 Vaccinations in the United Kingdom: Observational Study.

Background: The rollout of vaccines for COVID-19 in the United Kingdom started in December 2020. Uptake has been high, and there has been a subsequent reduction in infections, hospitalizations, and deaths among vaccinated individuals. However, vaccine hesitancy remains a concern, in particular relating to adverse effects following immunization (AEFIs).

A fuzzy-enhanced deep learning approach for early detection of Covid-19 pneumonia from portable chest X-ray images.

The Covid-19 pandemic is the defining global health crisis of our time. Chest X-Rays (CXR) have been an important imaging modality for assisting in the diagnosis and management of hospitalised Covid-19 patients. However, their interpretation is time intensive for radiologists.

Effectiveness of virtual and augmented reality for improving knowledge and skills in medical students: protocol for a systematic review.

Introduction: Virtual reality (VR) and augmented reality (AR) technologies are increasingly being used in undergraduate medical education. We aim to evaluate the effectiveness of VR and AR technologies for improving knowledge and skills in medical students.

Methods And Analysis: Using Best Evidence in Medical Education (BEME) collaboration guidelines, we will search MEDLINE (via PubMed), Education Resources Information Center, PsycINFO, Web of Knowledge, Embase and the Cochrane Central Register of Controlled Trials for English-language records, from January 1990 to March 2021.

Understanding Public Perceptions of COVID-19 Contact Tracing Apps: Artificial Intelligence-Enabled Social Media Analysis.

Background: The emergence of SARS-CoV-2 in late 2019 and its subsequent spread worldwide continues to be a global health crisis. Many governments consider contact tracing of citizens through apps installed on mobile phones as a key mechanism to contain the spread of SARS-CoV-2.

Objective: In this study, we sought to explore the suitability of artificial intelligence (AI)-enabled social media analyses using Facebook and Twitter to understand public perceptions of COVID-19 contact tracing apps in the United Kingdom.

Artificial Intelligence-Enabled Analysis of Public Attitudes on Facebook and Twitter Toward COVID-19 Vaccines in the United Kingdom and the United States: Observational Study.

Background: Global efforts toward the development and deployment of a vaccine for COVID-19 are rapidly advancing. To achieve herd immunity, widespread administration of vaccines is required, which necessitates significant cooperation from the general public. As such, it is crucial that governments and public health agencies understand public sentiments toward vaccines, which can help guide educational campaigns and other targeted policy interventions.

Real-Time Artifacts Reduction during TMS-EEG Co-Registration: A Comprehensive Review on Technologies and Procedures.

Transcranial magnetic stimulation (TMS) excites neurons in the cortex, and neural activity can be simultaneously recorded using electroencephalography (EEG). However, TMS-evoked EEG potentials (TEPs) do not only reflect transcranial neural stimulation as they can be contaminated by artifacts. Over the last two decades, significant developments in EEG amplifiers, TMS-compatible technology, customized hardware and open source software have enabled researchers to develop approaches which can substantially reduce TMS-induced artifacts.

Investigating the use of data-driven artificial intelligence in computerised decision support systems for health and social care: A systematic review.

There is growing interest in the potential of artificial intelligence to support decision-making in health and social care settings. There is, however, currently limited evidence of the effectiveness of these systems. The aim of this study was to investigate the effectiveness of artificial intelligence-based computerised decision support systems in health and social care settings.

National clinical practice guidelines for food allergy and anaphylaxis: an international assessment.

Background: Clinical practice guidelines are important tools to promote evidence-based clinical care, but not all countries have the capacity or infrastructure to develop these in-house. The European Academy of Allergy and Clinical Immunology has recently developed guidelines for the prevention, diagnosis and management of food allergy and the management of anaphylaxis. In order to inform dissemination, adaptation and implementation plans, we sought to identify countries that have/do not have national guidelines for food allergy and anaphylaxis.