{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T16:40:59Z","timestamp":1793378459014,"version":"4.5.0"},"reference-count":27,"publisher":"BMJ","issue":"4","license":[{"start":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T00:00:00Z","timestamp":1760486400000},"content-version":"unspecified","delay-in-days":14,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100014911","name":"Ministry of Defence of the Czech Republic","doi-asserted-by":"crossref","award":["MO1012"],"award-info":[{"award-number":["MO1012"]}],"id":[{"id":"10.13039\/501100014911","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Donatio Universitatis Carolinae","award":["Legal Support for New Technologies and Innovations"],"award-info":[{"award-number":["Legal Support for New Technologies and Innovations"]}]},{"DOI":"10.13039\/100007397","name":"Charles University","doi-asserted-by":"crossref","award":["Cooperatio Neuroscience"],"award-info":[{"award-number":["Cooperatio Neuroscience"]}],"id":[{"id":"10.13039\/100007397","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["Trauma Surg Acute Care Open"],"accepted":{"date-parts":[[2025,9,27]]},"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>AI support increased mean diagnostic accuracy by 9.4% (p&lt;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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Level of evidence<\/jats:title>\n                    <jats:p>II. Diagnostic Test.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/tsaco-2024-001700","type":"journal-article","created":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T10:15:18Z","timestamp":1760523318000},"page":"e001700","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":4,"title":["Artificial intelligence-assisted chest radiograph interpretation in Role 2 military field hospital settings: a controlled experimental study"],"prefix":"10.1136","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7725-5181","authenticated-orcid":false,"given":"Martin","family":"M\u00e1jovsk\u00fd","sequence":"first","affiliation":[{"name":"Department of Neurosurgery and Neurooncology, First Faculty of Medicine, Charles University and Military University Hospital, Praha, Czech 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