{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T20:21:36Z","timestamp":1793132496082,"version":"4.5.0"},"reference-count":35,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Med Inform"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec sec-type=\"background\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Rib fractures are present in 10%\u201015% of thoracic trauma cases but are often missed on chest radiographs, delaying diagnosis and treatment. Artificial intelligence (AI) may improve detection and triage in emergency settings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"objective\">\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aims to evaluate diagnostic accuracy, processing speed, and technical feasibility of an artificial intelligence\u2013assisted rib fracture detection system using prospectively collected data within a real-world, high-volume emergency department workflow.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"methods\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted an observational feasibility study with prospective data collection of a faster region-based convolutional neural network\u2013based AI model deployed in the emergency department to analyze 23,251 real-world chest radiographs (22,946 anteroposterior; 305 oblique) from April 1 to July 2, 2023. This study was approved by the Institutional Review Board of MacKay Memorial Hospital (IRB No. 20MMHIS483e). AI operated passively, without influencing clinical decision-making. The reference standard was the final report issued by board-certified radiologists. A subset of discordant cases underwent post hoc computed tomography review for exploratory analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      AI achieved 74.5% sensitivity (95% CI 0.708-0.780), 93.3% specificity (95% CI 0.930-0.937), 24.2% positive predictive value, and 99.2% negative predictive value. Median inference time was 10.6 seconds versus 3.3 hours for radiologist reports (paired Wilcoxon signed-rank test\n                      <jats:italic>W<\/jats:italic>\n                      =112 987.5,\n                      <jats:italic>P<\/jats:italic>\n                      &lt;.001). The analysis revealed peak imaging demand between 08:00 and 16:00 and Thursday-Saturday evenings. A 14-day graphics processing unit outage underscored the importance of infrastructure resilience.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"conclusions\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The AI system demonstrated strong technical feasibility for real-time rib fracture detection in a high-volume emergency department setting, with rapid inference and stable performance during prospective deployment. Although the system showed high negative predictive value, the observed false-positive and false-negative rates indicate that it should be considered a supportive screening tool rather than a stand-alone diagnostic solution or a replacement for clinical judgment. These findings support further clinician-in-the-loop studies to evaluate clinical feasibility, workflow integration, and impact on diagnostic decision-making. However, interpretation is limited by reliance on radiology reports as the reference standard and the system\u2019s passive, non-interventional deployment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/77965","type":"journal-article","created":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T04:23:47Z","timestamp":1769747027000},"page":"e77965-e77965","source":"Crossref","is-referenced-by-count":6,"title":["Prospective Diagnostic Accuracy and Technical Feasibility of Artificial Intelligence-Assisted Rib Fracture Detection on Chest Radiographs: Observational Study"],"prefix":"10.2196","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4087-678X","authenticated-orcid":false,"given":"Shu-Tien","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4891-6299","authenticated-orcid":false,"given":"Liong-Rung","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6185-6270","authenticated-orcid":false,"given":"Ming-Feng","family":"Tsai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1501-9893","authenticated-orcid":false,"given":"Ming-Yuan","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6919-8199","authenticated-orcid":false,"given":"Hung-Wen","family":"Chiu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,1,29]]},"reference":[{"issue":"3","key":"R1","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2020.1316","article-title":"Association between adherence to evidence-based practices for treatment of patients with traumatic rib fractures and mortality rates among US trauma centers","volume":"3","author":"Tignanelli","journal-title":"JAMA Netw Open"},{"issue":"4","key":"R2","doi-asserted-by":"publisher","first-page":"268","DOI":"10.20408\/jti.2021.0096","article-title":"Outcomes after rib fractures: more complex than a single number","volume":"35","author":"Colling","journal-title":"J Trauma Inj"},{"issue":"4","key":"R3","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1177\/000313480807400406","article-title":"Perils of rib fractures","volume":"74","author":"Sharma","journal-title":"Am Surg"},{"issue":"6","key":"R4","doi-asserted-by":"publisher","first-page":"1603","DOI":"10.2214\/ajr.173.6.10584808","article-title":"Sonography compared with radiography in revealing acute rib fracture","volume":"173","author":"Griffith","journal-title":"AJR Am J Roentgenol"},{"issue":"4","key":"R5","doi-asserted-by":"publisher","first-page":"384","DOI":"10.21037\/qims.2017.08.02","article-title":"Thoracic wall trauma-misdiagnosed lesions on radiographs and usefulness of ultrasound, multidetector computed tomography and magnetic resonance imaging","volume":"7","author":"Tomas","journal-title":"Quant Imaging Med Surg"},{"issue":"4","key":"R6","doi-asserted-by":"publisher","DOI":"10.5812\/atr.37070","article-title":"Clinical utility of chest computed tomography in patients with rib fractures CT chest and rib fractures","volume":"5","author":"Chapman","journal-title":"Arch Trauma Res"},{"key":"R7","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2023.1178798","article-title":"Deep convolutional neural network for rib fracture recognition on chest radiographs","volume":"10","author":"Huang","journal-title":"Front Med"},{"issue":"4","key":"R8","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1016\/j.cjtee.2016.04.010","article-title":"Comparison of ultrasonography and radiography in diagnosis of rib fractures","volume":"20","author":"Pishbin","journal-title":"Chin J Traumatol"},{"issue":"22","key":"R9","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1001\/jama.2016.17216","article-title":"Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs","volume":"316","author":"Gulshan","journal-title":"JAMA"},{"key":"R10","unstructured":"Rajpurkar P Irvin J Zhu K et al. 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