SynthesisEmergency radiology2026
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.
Synthesis in Emergency radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
7 authors.
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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. Primary analyses evaluated diagnostic accuracy (sensitivity and specificity) of stand-alone AI based on (1) cohort type (selected vs. unselected), (2) test dataset origin (internal vs. external), and (3) level of analysis (patient-wise, vertebra-wise, rib-wise). Secondary analyses explored accuracy differences according to (1) CAAI-FDS, (2) anatomical region and (3) reader type (stand-alone AI, human unaided, human aided by AI). Forest plots visualized results, and heterogeneity was measured using generalized I
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