We are pleased to share article titled „AI-Assisted Fracture Detection in Orthopedic and Trauma Imaging: Where It Works, Where It Fails, and Principles for Safe Clinical Deployment”, published in the Diagnostics 2026, 16(10), 1420, authored by Wojciech Michał Glinkowski (Center of Excellence “TeleOrto” for Telediagnostics and Treatment of Disorders and Injuries of the Locomotor System, Department of Medical Informatics and Telemedicine, Medical University of Warsaw; President of the Polish Telemedicine Society), Paweł Kaminski (Clinic of Locomotor Disorders, Andrzej Frycz Modrzewski University, Kraków, and Malopolan Orthopedic and Rehabilitation Hospital, Kraków), and Rafał Obuchowicz (Department of Diagnostic Imaging, Jagiellonian University Medical College, Kraków). Article is available online at https://doi.org/10.3390/diagnostics16101420.
Abstract
Background: Missed fractures on initial imaging assessments remain a clinically significant source of diagnostic errors in orthopedic and trauma care. AI-assisted imaging tools are increasingly integrated into fracture detection workflows. However, their diagnostic benefits and safety vary substantially across anatomical regions, clinical contexts, and levels of reader experience.
Purpose: To synthesize the current evidence on the diagnostic impact of AI-assisted fracture detection and to discuss evidence-informed principles for safe and selective clinical deployment.
Methods: A structured narrative synthesis of meta-analyses, multi-reader, multi-case observer studies, and real-world implementation investigations was performed. Diagnostic performance patterns were examined across anatomical regions and levels of reader experience. No quantitative pooling or reanalysis of the primary data was performed. The findings were synthesized across anatomical regions, reader-experience groups, and implementation-relevant clinical contexts.
Results: Across studies, AI-assisted interpretation was generally associated with moderate gains in sensitivity and lower missed-fracture rates compared with unaided human reading, while largely preserving specificity. The diagnostic benefit was greatest among less-experienced readers in high-volume emergency settings. Performance was strongly anatomy-dependent: consistent and clinically meaningful improvements were observed for hip and appendicular skeleton fractures; intermediate benefits with increased false-positive burden were reported for wrist and rib fractures; and inferior sensitivity relative to expert interpretation was documented for cervical and vertebral spine injuries.
Conclusions: AI-assisted fracture detection improves diagnostic safety when implemented as a structured second-reader tool; however, its effectiveness depends heavily on anatomy. Available evidence supports selective, risk-stratified deployment, guided by anatomy-specific risk considerations and supervised clinical use, rather than indiscriminate or autonomous use, to maximize benefits and minimize patient safety risks in orthopedic and trauma imaging.