Artificial intelligence for pediatric fracture detection: impact on diagnostic revisions and patient recall rates in a tertiary emergency setting

Purpose Artificial intelligence (AI) can support and enhance radiologists in musculoskeletal imaging, but evidence of clinical benefit is still lacking. This prospective study aimed to estimate the range of expected effect size on patient recall rates whether after implementation of an AI system for fracture detection affects in a paediatric emergency setting during out-of-hourse care. Methods Children and adolescents (2–18 years) undergoing appendicular skeletal radiography between April and September 2025 during on-call hours at a tertiary referral hospital were eligible. On every second day, automated fracture detection by a commercial AI system (TechCare Kids, Milvue, Paris, France) was available for the treating physician. Endpoints were the rate of diagnostic revisions leading to patient recall the next day, therapeutic changes, length of stay at the emergency department, need for senior consultation, subjective diagnostic confidence, and AI accuracy. Results Among 1515 screened patients, 667 were enrolled (median age 11.0 years, 61% male). Fractures were present in 296 cases (44.3%). AI accuracy was 95.1%. Diagnostic revisions occurred in 8.6% without AI, in 5.7% with AI support (risk ratio 0.66; 95% CI 0.37–1.19). Resulting therapeutic changes were rare in both groups (2.0% vs. 0.4%; p = 0.10). No significant differences were observed for secondary endpoints, including need for senior consultation (14.3% vs. 12.5%), subjective diagnostic confidence, or length of stay (2.3 h vs. 2.4 h). Conclusion Given the small effect size for patient recall rates in an academic medical setting, it is questionable whether the efforts conducting a confirmatory study with adequate statistical power would be justified by the expected incremental benefit for the investigated outcomes.