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Many publications describe powerful AI-enabled algorithms. Yet there\u2019s only scarce evidence for measurable value in terms of patient outcomes, clinical decision-making or socio-economic impact. Our aim was to investigate the significance of AI in the emergency treatment of wrist trauma patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>Two groups of physicians were confronted with twenty realistic cases of wrist trauma patients and had to find the correct diagnosis and provide a treatment recommendation. One group was assisted by an AI-enabled application which detects and localizes distal radius fractures (DRF) with near-to-perfect precision while the other group had no help. Primary outcome measurement was diagnostic accuracy. Secondary outcome measurements were required time, number of added CT scans and senior consultations, correctness of the treatment, subjective and objective stress levels.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The AI-supported group was able to make a diagnosis without support (no additional\u00a0CT, no senior consultation) in significantly more of the cases than the control group (75% vs. 52%,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.003). The AI-enhanced group detected DRF with superior sensitivity (1.00 vs. 0.96,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.06) and specificity (0.99 vs. 0.93,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.17), used significantly less additional\u00a0CT scans to reach the correct diagnosis (14% vs. 28%,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.02) and was subjectively significantly less stressed (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.05).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The results indicate that physicians can diagnose wrist trauma more accurately and faster when aided by an AI-tool that lessens the need for extra diagnostic procedures. The AI-tool also seems to lower physicians' stress levels while examining cases. We anticipate that these benefits will be amplified in larger studies as skepticism towards the new technology diminishes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s13018-024-05063-6","type":"journal-article","created":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T23:33:55Z","timestamp":1726702435000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["The potential benefit of artificial intelligence regarding clinical decision-making in the treatment of wrist trauma patients"],"prefix":"10.1186","volume":"19","author":[{"given":"Marco","family":"Keller","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meret","family":"Rohner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philipp","family":"Honigmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,9,19]]},"reference":[{"issue":"2","key":"5063_CR1","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1148\/118.2.257","volume":"118","author":"JL Lehr","year":"1976","unstructured":"Lehr JL, Lodwick GS, Farrell C, Braaten MO, Virtama P, Kolvisto EL. 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