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TRV-2026-1145Version 1 · Certified

Written 2026-09-19 06:54:07 UTC · current record

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TRUVACE RECORD VERSION
record: TRV-2026-1145
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-09-19T06:54:07.676966Z
status: published
lens: g_space
sector: health
headline: Diagnostic accuracy of AI-assisted versus independent physician interpretation for bone fractures: a systematic review and meta-analysis
dek: 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…
gain_title: 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%).
problem_title: (none)
trace_subject: (none)
gain_reading: 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%).
gain_evidence: (none)
problem_reading: (none)
problem_evidence: (none)
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.
limitation: 
tag: Evidence-backed gain
key_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.
rundown: 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.
sources:
- peer_reviewed | European Radiology Experimental | https://doi.org/10.1186/s41747-026-00803-1 | 2026-09-17
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