− HEALTH Agentic artificial intelligence (AI) is an emerging development in the digital transformation of healthcare.…− HEALTH Artificial intelligence (AI) is expanding the capacity of public health systems to detect infectious disease…+ HEALTH Purpose To estimate diagnostic performance of a deep learning algorithm for extremity fracture detection on r…− HEALTH Objective Hypomimia, or reduced facial expressiveness, is a cardinal motor feature of Parkinson's disease (PD…− SCIENCE Background Generative artificial intelligence (GenAI) chatbots have shown utility in assisting with various r…− HEALTH ObjectiveTo critically review the current pharmacologic treatment landscape for epidermal growth factor recep…− HEALTH Her name was June. June would be the last month she ever knew. I remember the way her bed made her so small,…− LIFESTYLE Precision nutrition on a global scale necessitates an understanding of food not as static collections of so-c…
TruaceTracing the truth around AIWednesday, September 23, 2026
Subscribe
Health·G Space·Evidence-backed gain·Published 2026-09-23

Diagnostic accuracy & clinical importance of AI confidence for extremity fracture detection: 2,508-patient retrospective cohort

Abstract: Purpose To estimate diagnostic performance of a deep learning algorithm for extremity fracture detection on radiographs in patients aged ≥ 2 years using a refined reference standard. Secondary, to compare positive predictive value (PPV) by the algorithm's built-in confidence level (high vs. low) and diagnostic performance between adults (≥ 18 years) and children. Methods This retrospective single-center study consecutively included patients with radiography of a suspected extremity fracture between January and D…

TRV-2026-1176Peer-reviewedPermanent record — cite & verify
Diagnostic accuracy & clinical importance of AI confidence for extremity fracture detection: 2,508-patient retrospective cohort

Hospital Universitari Doctor Peset, València 04 by 19Tarrestnom65. CC BY-SA 4.0 · https://creativecommons.org/licenses/by-sa/4.0

The quick read

Purpose To estimate diagnostic performance of a deep learning algorithm for extremity fracture detection on radiographs in patients aged ≥ 2 years using a refined reference standard. Secondary, to compare positive predictive value (PPV) by the algorithm's built-in confidence level (high vs. low) and diagnostic performance between adults (≥ 18 years) and children.

The index test was the algorithm output (bounding boxes with confidence). Sensitivity, specificity, PPV and negative predictive value (NPV) were calculated with 95% confidence intervals; differences were tested using the McNemar and a score test and patient-clustered logistic regression.

Main points
  • Purpose To estimate diagnostic performance of a deep learning algorithm for extremity fracture detection on radiographs in patients aged ≥ 2 years using a refined reference standard.
  • Secondary, to compare positive predictive value (PPV) by the algorithm's built-in confidence level (high vs. low) and diagnostic performance between adults (≥ 18 years) and children.
  • Methods This retrospective single-center study consecutively included patients with radiography of a suspected extremity fracture between January and December 2024.
Gain

PPV for high-confidence was superior to low-confidence detections (99.1% vs 59.1%, p Conclusion The algorithm showed good diagnostic performance for extremity fracture detection on radiographs.

The rundown

Methods This retrospective single-center study consecutively included patients with radiography of a suspected extremity fracture between January and December 2024. The index test was the algorithm output (bounding boxes with confidence).

Sources

Reader signal

How should this claim be treated?

The debate