{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T16:08:04Z","timestamp":1787846884361,"version":"build-2784847793"},"reference-count":20,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["XST"],"published-print":{"date-parts":[[2024,10,16]]},"abstract":"<jats:p>BACKGROUND: Accurately detecting a variety of lung abnormalities from heterogenous chest X-ray (CXR) images and writing radiology reports is often difficult and time-consuming. OBJECTIVE: To access the utility of a novel artificial intelligence (AI) system (MOM-ClaSeg) in enhancing the accuracy and efficiency of radiologists in detecting heterogenous lung abnormalities through a multi-reader and multi-case (MRMC) observer performance study. METHODS: Over 36,000 CXR images were retrospectively collected from 12 hospitals over 4 months and used as the experiment group and the control group. In the control group, a double reading method is used in which two radiologists interpret CXR to generate a final report, while in the experiment group, one radiologist generates the final reports based on AI-generated reports. RESULTS: Compared with double reading, the diagnostic accuracy and sensitivity of single reading with AI increases significantly by 1.49% and 10.95%, respectively (P\u200a&lt; \u200a0.001), while the difference in specificity is small (0.22%) and without statistical significance (P\u200a=\u200a0.255). Additionally, the average image reading and diagnostic time in the experimental group is reduced by 54.70% (P\u200a&lt; \u200a0.001). CONCLUSION: This MRMC study demonstrates that MOM-ClaSeg can potentially serve as the first reader to generate the initial diagnostic reports, with a radiologist only reviewing and making minor modifications (if needed) to arrive at the final decision. It also shows that single reading with AI can achieve a higher diagnostic accuracy and efficiency than double reading.<\/jats:p>","DOI":"10.3233\/xst-240051","type":"journal-article","created":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T10:55:31Z","timestamp":1729248931000},"page":"1-16","source":"Crossref","is-referenced-by-count":1,"title":["Can AI generate diagnostic reports for radiologist approval on CXR images? 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