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Health·G Space·Evidence-backed gain·Published 2026-09-19

Diagnostic accuracy of AI-assisted versus independent physician interpretation for bone fractures: a systematic review and meta-analysis

Abstract: Objective We systematically evaluated the diagnostic performance of artificial intelligence (AI)-assisted interpretation versus independent physician assessment for fracture detection. Materials and methods Adhering to PRISMA-DTA guidelines, we searched PubMed and Web of Science for original studies published up to September 17, 2025. Quality was assessed utilizing the QUADAS-3 framework. A bivariate random-effects model pooled diagnostic metrics. Accuracy was assessed by summary receiver operating characteristi…

TRV-2026-1145Peer-reviewedPermanent record — cite & verify
Diagnostic accuracy of AI-assisted versus independent physician interpretation for bone fractures: a systematic review and meta-analysis

Hospital Universitari Doctor Peset, València 05 by 19Tarrestnom65. CC BY-SA 4.0 · https://creativecommons.org/licenses/by-sa/4.0

The quick read

Objective We systematically evaluated the diagnostic performance of artificial intelligence (AI)-assisted interpretation versus independent physician assessment for fracture detection. Materials and methods Adhering to PRISMA-DTA guidelines, we searched PubMed and Web of Science for original studies published up to September 17, 2025.

Compared to unassisted diagnosis, AI significantly improved pooled sensitivity (87%, 95% confidence interval [CI]: 84-89%, versus 73%, 95% CI: 69-78%) and maintained high specificity (95%, 95% CI: 92-97%, versus 94%, 95% CI: 89-96%). Subgroup analysis revealed junior clinicians derived the greatest benefit, exhibiting a 21% absolute sensitivity increase.

Main points
  • Objective We systematically evaluated the diagnostic performance of artificial intelligence (AI)-assisted interpretation versus independent physician assessment for fracture detection.
  • Materials and methods Adhering to PRISMA-DTA guidelines, we searched PubMed and Web of Science for original studies published up to September 17, 2025.
  • Quality was assessed utilizing the QUADAS-3 framework.
Gain

Compared to unassisted diagnosis, AI significantly improved pooled sensitivity (87%, 95% confidence interval [CI]: 84-89%, versus 73%, 95% CI: 69-78%) and maintained high specificity (95%, 95% CI: 92-97%, versus 94%, 95% CI: 89-96%).

The rundown

Quality was assessed utilizing the QUADAS-3 framework. A bivariate random-effects model pooled diagnostic metrics.

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