{"id":"AIBD-0655","title":"NICE finds that AI fracture-detection software raised clinicians' sensitivity on X-rays and allows four systems in NHS urgent care","summary":"NICE reviewed 16 studies of AI fracture software such as BoneView and found that readers' sensitivity rose without lost specificity, from 61% to 74% in one study.","date_observed":"2025-01-14","date_basis":"source publication","date_precision":"day","benefit_status":"reported","source_collection":null,"source_entry":null,"account":"NICE notes that missed fractures are reported to be the most common diagnostic error in emergency departments. The National Institute for Health and Care Excellence (NICE) assessed AI software that marks suspected fractures on X-rays for the clinician who reads them. Its external assessment group found 16 eligible studies, most of them on BoneView and RBfracture. In these studies, clinicians who read with the software detected more fractures without a loss of specificity. In a BoneView study of emergency physicians, sensitivity rose from 61% to 74%. RBfracture raised it from 74% to 83%, and Rayvolve from 79% to 94%. In guidance published on 14 January 2025, the committee concluded that the evidence suggests that these technologies have the potential to improve fracture detection, and it allowed Rayvolve, TechCare Alert, BoneView and RBfracture in NHS urgent care while more evidence is generated. Most studies were retrospective case-control reader studies, none took place in UK urgent care, and reading times changed by only a few seconds. The committee noted that unassisted readers were less accurate than expected, so the gain may be overstated.","category":"general benefit","unexpected":false,"purpose":"The systems were built to help clinicians detect fractures on X-rays. 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Artificial intelligence (AI) technologies to help detect fractures on X-rays in urgent care: early value assessment. HealthTech guidance HTG739, section 3: Committee discussion. 14 January 2025.","title":"Artificial intelligence (AI) technologies to help detect fractures on X-rays in urgent care: committee discussion","url":"https://www.nice.org.uk/guidance/htg739/chapter/3-Committee-discussion","date":"2025-01-14","written_by":"independent","quote":"Diagnostic accuracy studies typically found improved sensitivity of fracture detection, without reduced specificity, by healthcare professionals assisted by AI software compared with unassisted interpretation."},{"kind":"regulatory document","citation":"National Institute for Health and Care Excellence. HTG739, section 1: Recommendations. 14 January 2025.","title":"Artificial intelligence (AI) technologies to help detect fractures on X-rays in urgent care: recommendations","url":"https://www.nice.org.uk/guidance/htg739/chapter/1-Recommendations","date":"2025-01-14","written_by":"independent","quote":"Four artificial intelligence (AI) technologies can be used in the NHS during the evidence generation period as options to help healthcare professionals detect fractures on X-rays in urgent care."},{"kind":"peer-reviewed study","citation":"Duron L, Ducarouge A, Gillibert A, et al. Assessment of an AI Aid in Detection of Adult Appendicular Skeletal Fractures by Emergency Physicians and Radiologists: A Multicenter Cross-sectional Diagnostic Study. Radiology. 2021;300(1):120-129.","title":"Assessment of an AI Aid in Detection of Adult Appendicular Skeletal Fractures by Emergency Physicians and Radiologists","url":"https://pubmed.ncbi.nlm.nih.gov/33944629/","date":"2021-05-04","written_by":"makers","quote":"The AI aid improved the sensitivity of physicians by 8.7% (95% CI: 3.1, 14.2; P = .003 for superiority)"}],"replication":{"produced_by":["The software marked suspected fractures on the X-ray for the reader.","A trained clinician made the final interpretation."],"must_hold":["Clinicians in urgent care read with the software as they did in the studies.","The software performs on local X-ray equipment as it did in testing."],"echoes":[{"date":"2025-12-30","setting":"Meta-analysis of 26 studies of AI-assisted fracture detection on radiographs","model_version":null,"outcome":"recurred","source":"https://pmc.ncbi.nlm.nih.gov/articles/PMC12795274/"}]},"paired_incident":null,"headline":"NICE backs AI help for spotting fractures on X-rays","metric":"from 61% to 74%","comparator":{"against":"Emergency physicians reading mixed fracture types without BoneView","before":"61% sensitivity","after":"74% sensitivity","basis":"measured","quote":"indicated that sensitivity increased from 61% (unassisted) to 74% (assisted)","url":"https://www.nice.org.uk/guidance/htg739/chapter/3-Committee-discussion"},"later":null,"value":null,"developer_country":"Multiple: France, Denmark","deployer_country":"Multiple","unrated_because":null,"brief_notes":null,"brief":null,"revisions":[{"date":"2026-10-29","change":"Added the case.","source":"https://www.nice.org.uk/guidance/htg739/chapter/3-Committee-discussion","editor":"AIBD"}],"confidence":"found","shade":"high","claim_status":"Found by a regulator","claim_basis":"Sources: two independent regulatory documents and the makers' peer-reviewed study. 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