ArticleTrauma surgery & acute care open2025
Artificial intelligence-assisted chest radiograph interpretation in Role 2 military field hospital settings: a controlled experimental study.
Article in Trauma surgery & acute care open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Artificial Intelligence-Assisted Quantification of Longitudinal HRCT Changes During Treatment of Pulmonary Tuberculosis: An Exploratory Proof-of-Concept Study.Diagnostics (Basel, Switzerland) · 2026Article
- Context matters: evaluating AI-assisted chest radiograph interpretation in military field hospitals.Trauma surgery & acute care open · 2025Article
Corrections and comments
- Commented on by
Authors and funding
23 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Forward military field hospitals often operate in battle zone environments where access to specialized personnel, such as radiologists, is limited, complicating the accuracy of diagnostic imaging. Chest radiographs are crucial for assessing thoracic injuries and other conditions, but their interpretation frequently falls to non-radiologist personnel. This study evaluates the effectiveness of an artificial intelligence (AI)-assisted model in enhancing the diagnostic accuracy of chest radiographs in such resource-limited settings. Methods: Nine board-certified military physicians from various non-radiology specialties interpreted 159 anonymized chest radiographs, both with and without the support of AI. The AI model, INSIGHT CXR, generated automated descriptions for 80 radiographs, whereas 79 were interpreted without AI support. A linear mixed-effects model was used to assess the difference in diagnostic accuracy between the two conditions. Secondary analyses examined the effects of radiograph type and physician specialty on diagnostic performance. Results: AI support increased mean diagnostic accuracy by 9.4% (p<0.001) from pretest to post-test, representing a 23.15% relative improvement. This improvement was consistent across both normal and abnormal findings, with no significant differences observed based on radiograph type or physician specialty. These findings suggest that AI tools can serve as effective support in field hospitals, improving diagnostic precision and decision-making in the absence of radiologists. Conclusions: This study highlights the potential for AI-assisted radiograph interpretation to enhance diagnostic accuracy in military field hospitals. If AI tools are proven reliable, they could be integrated into the workflow of forward field hospitals, improving the quality of care for injured personnel. Immediate benefits may include faster diagnoses, increased personnel readiness, optimized performance, and cost savings, leading to better outcomes in combat operations. Level of evidence: II. Diagnostic Test.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.