Artificial intelligence for fracture detection on computed tomography: a comprehensive systematic review and meta-analysis of diagnostic test accuracy in non-commercial and commercial solutions
Abstract
Rising patient volumes, the increasing use of computed tomography (CT) imaging in emergency departments and the resulting prolonged waiting times highlight the urgent need for efficient and accurate diagnostic tools, especially given that the number of experienced healthcare professionals is not increasing at the same pace. Artificial intelligence (AI) has emerged as a promising tool to support fracture detection on CT scans, with the potential to streamline diagnostic workflows in emergency care. However, concerns exist regarding dataset bias, limited external testing, and methodological variability. This systematic review and diagnostic test accuracy (DTA) meta-analysis aimed to comprehensively assess the diagnostic accuracy of AI-driven fracture detection solutions, with a particular focus on the effect of the testing strategy, cohort composition and commercial availability on diagnostic accuracy. The Cochrane Handbook for Systematic Reviews of DTA and reported according to PRISMA-DTA guidelines were followed. We systematically searched Embase, MEDLINE, Cochrane Library, Web of Science, and Google Scholar for studies published from January 2010 onward, complemented by citation chasing and manual searches for commercial AI fracture detection solutions (CAAI-FDS). Two reviewers independently conducted study selection, data extraction, and risk of bias assessment using a modified QUADAS-2 tool. Statistical analysis was conducted using STATA 18.1 and the -metadta- command.…
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