Systematic assessment of the medical utility of radiology and diagnostic Artificial Intelligence in fracture detection (SAMURAI-fracture): a protocol for a multicentre cluster-randomised controlled trial
Abstract
Introduction: Fracture misdiagnosis is a common diagnostic error in emergency departments (EDs) and minor injury units (MIUs), leading to poor patient outcomes, unnecessary treatments and significant healthcare costs. Artificial intelligence (AI)-assisted fracture detection tools are now available for use in radiology workflows; however, the impact of these technologies on patient outcomes, experiences and overall care pathways in the real-world clinical setting is limited.
Methods and analysis: We will conduct a prospective cluster randomised cross-over trial over a 6-month period, assessing the impact of an AI-assisted fracture detection tool in EDs and MIUs across 4 healthcare Trusts in the UK. Patients aged over 2 years old undergoing a plain film radiography for a suspected fracture as part of routine clinical care will be eligible for study enrolment. The trial will deploy Radiobotics' RBfracture, a CE-approved AI-assisted medical device software for fracture detection at each site for 6 months. Randomisation will be at a cluster-level; a site will be randomised to begin with 'AI on' or 'AI off' for a month, followed by alternating active status each month for the remaining 5 months. The primary outcome will evaluate the incidence of 'inappropriate healthcare contacts' among patients receiving imaging for suspected fractures. This composite metric encompasses inappropriate referrals to fracture clinics, repeated hospital attendances and subsequent follow-up…
How strong is the evidence?
Evidence Hub reads this as a randomized trial, done in people. Its evidence level is 2 of 5 (High · randomized trial). Randomized trials of a reasonable size are the strongest single-study design.
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