Emerging technology solutions to support national emergency workforce capacity-building initiatives: lessons from Ugandan policy and practice.

Trauma and emergency care is a national priority in Uganda due to the high burden of injury, impacting a primarily young and rural population. With a significant gap in qualified emergency medicine professionals, a need exists to rapidly upskill the current health workforce and to strengthen access to learning for non-specialist emergency care providers nationally. This review was completed in partnership with the Ugandan Ministry of Health and a consortium of UK partners to support national emergency workforce capacity building in Uganda and East Africa.

Can machine learning assist in systemic sclerosis diagnosis and management? A scoping review.

This scoping review aims to summarize the existing literature on how machine learning can be used to impact systemic sclerosis diagnosis, management, and treatment. Following Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) reporting guidelines, Embase, Web of Science, Medline (PubMed), IEEE Xplore, and ACM Digital Library were searched from inception to 3 March 2024, for primary literature reporting on machine learning models in any capacity regarding scleroderma. Following robust triaging, 11 retrospective studies were included in this scoping review.

Predicting psoriasis severity using machine learning: A systematic review.

In dermatology, the applications of machine learning (ML), an artificial intelligence (AI) subset that enables machines to learn from experience, have progressed past the diagnosis and classification of skin lesions. A lack of systematic reviews exists to explore the role of ML in predicting the severity of psoriasis. This systematic review aims to identify and summarize the existing literature on predicting psoriasis severity using ML algorithms and identify gaps in current clinical applications of these tools.

Corrigendum: Plasticity of carbohydrate transport at the blood-brain barrier.

[This corrects the article DOI: 10.3389/fnbeh.2020.

Applications of Machine Learning in Prognostication of mTBI: A Systematic Review.

Objective: To review the literature regarding the current state and clinical applicability of machine learning (ML) models in prognosticating the outcomes of patients with mild traumatic brain injury (mTBI) in the early clinical presentation.

Design: Databases were searched for studies including ML and mTBI from inception to March 10, 2023. Included studies had a primary outcome of predicting post-mTBI prognosis or sequalae.

Diagnosing contact dermatitis using machine learning: A review.

Background: Machine learning (ML) offers an opportunity in contact dermatitis (CD) research, where with full clinical picture, may support diagnosis and patch test accuracy.

Objective: This review aims to summarise the existing literature on how ML can be applied to CD in its entirety.

Methods: Embase, Medline, IEEE Xplore, and ACM Digital Library were searched from inception to February 7, 2024, for primary literature reporting on ML models in CD.