Retrospective Study: AI Tool Accurately Excludes Pathology in Up to 52.7% of Unremarkable Chest Radiographs with Low Critical Misses – Radiology

Study Design and Population: This retrospective study assessed the effectiveness of a commercial AI tool in correctly identifying unremarkable chest radiographs, thus potentially reducing the workload in radiology departments. The study analyzed 1,961 chest radiographs from adult patients (median age: 72 years) across four Danish hospitals. The radiographs were labeled as remarkable or unremarkable by thoracic radiologists, and the AI tool’s performance was evaluated at varying sensitivity thresholds.

Main Findings: The AI tool demonstrated a specificity of 24.5% to 52.7% at sensitivity thresholds of 99.9% to 98.0%, respectively, effectively excluding pathology in unremarkable chest radiographs. At sensitivities of 95.4% or higher, the AI had equal or lower rates of critical misses compared to radiology reports, with the AI missing only 2.2% of critical findings compared to 1.1% by radiologists at similar sensitivity levels.

Implications for Practice: The results suggest that AI tools could autonomously report up to 52.7% of unremarkable chest radiographs, potentially reducing radiologist workload without compromising patient safety. However, prospective studies are necessary to confirm these findings and optimize AI deployment in clinical practice.

Reference: Plesner LL, Müller FC, Brejnebøl MW, et al. (2024). Using AI to Identify Unremarkable Chest Radiographs for Automatic Reporting. Radiology, 312(2), e240272. DOI: https://doi.org/10.1148/radiol.240272