Artificial intelligence-assisted chest radiograph interpretation in Role 2 military field hospital settings: a controlled experimental study

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61383082%3A_____%2F25%3A00001542" target="_blank" >RIV/61383082:_____/25:00001542 - isvavai.cz</a>

Alternative codes found

RIV/02819180:_____/25:#0000142 RIV/68407700:21460/25:00390355 RIV/00216208:11110/25:10504314 RIV/00216208:11220/25:10504314 RIV/00216208:11510/25:10504314

Result on the web

<a href="https://pubmed.ncbi.nlm.nih.gov/41113659/" target="_blank" >https://pubmed.ncbi.nlm.nih.gov/41113659/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1136/tsaco-2024-001700" target="_blank" >10.1136/tsaco-2024-001700</a>

Result language

angličtina

Original language name

Artificial intelligence-assisted chest radiograph interpretation in Role 2 military field hospital settings: a controlled experimental study

Original language description

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. © Author(s) (or their employer(s)) 2025. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

30221 - Critical care medicine and Emergency medicine

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Trauma Surgery and Acute Care Open

ISSN

2397-5776

e-ISSN

—

Volume of the periodical

10

Issue of the periodical within the volume

4

Country of publishing house

GB - UNITED KINGDOM

Number of pages

10

Pages from-to

1-10

UT code for WoS article

001596265300001

EID of the result in the Scopus database

2-s2.0-105019375994