Evaluating the impact of artificial intelligence-assisted image analysis on the diagnostic accuracy of front-line clinicians in detecting fractures on plain X-rays (FRACT-AI): protocol for a prospective observational study.

Introduction: Missed fractures are the most frequent diagnostic error attributed to clinicians in UK emergency departments and a significant cause of patient morbidity. Recently, advances in computer vision have led to artificial intelligence (AI)-enhanced model developments, which can support clinicians in the detection of fractures. Previous research has shown these models to have promising effects on diagnostic performance, but their impact on the diagnostic accuracy of clinicians in the National Health Service (NHS) setting has not yet been fully evaluated.

Risk of surgical site infection in hand trauma, and the impact of the SARS-CoV-2 pandemic: A cohort study.

Background: Despite the ubiquity of hand trauma, there remains insufficient published data to reliably inform these patients of surgical site infection (SSI) risk. We describe the risk of SSI in a single-centre cohort of patients with hand trauma, with an analysis of the impact of the coronavirus disease-2019 (COVID-19) pandemic.

Methods: Retrospective data collection of consecutive patients who underwent surgery for hand and wrist trauma in a single plastic surgery centre over two, three-month periods.