{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T03:00:02Z","timestamp":1788490802508,"version":"build-2803163510"},"reference-count":76,"publisher":"Bioscientifica","issue":"10","license":[{"start":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T00:00:00Z","timestamp":1770940800000},"content-version":"vor","delay-in-days":1961,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["journals.bioscientifica.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Artificial Intelligence (AI) in general, and Machine Learning (ML)-based applications in particular, have the potential to change the scope of healthcare, including orthopaedic surgery. The greatest benefit of ML is in its ability to learn from real-world clinical use and experience, and thereby its capability to improve its own performance. Many successful applications are known in orthopaedics, but have yet to be adopted and evaluated for accuracy and efficacy in patients\u2019 care and doctors\u2019 workflows. The recent hype around AI triggered hope for development of better risk stratification tools to personalize orthopaedics in all subsequent steps of care, from diagnosis to treatment. Computer vision applications for fracture recognition show promising results to support decision-making, overcome bias, process high-volume workloads without fatigue, and hold the promise of even outperforming doctors in certain tasks. In the near future, AI-derived applications are very likely to assist orthopaedic surgeons rather than replace us. \u2018If the computer takes over the simple stuff, doctors will have more time again to practice the art of medicine\u2019.76<\/jats:p>\n                  <jats:p>Cite this article: EFORT Open Rev 2020;5:593-603. DOI: 10.1302\/2058-5241.5.190092<\/jats:p>","DOI":"10.1302\/2058-5241.5.190092","type":"journal-article","created":{"date-parts":[[2020,10,26]],"date-time":"2020-10-26T04:24:20Z","timestamp":1603686260000},"page":"593-603","update-policy":"https:\/\/doi.org\/10.1530\/crossmarkpolicy-16","source":"Crossref","is-referenced-by-count":76,"title":["Artificial intelligence in orthopaedics: false hope or not? A narrative review along the line of Gartner\u2019s hype cycle"],"prefix":"10.1530","volume":"5","author":[{"given":"Jacobien H.F.","family":"Oosterhoff","sequence":"first","affiliation":[{"name":"1Department of Orthopaedic Surgery, Amsterdam UMC, University of Amsterdam, the Netherlands"},{"name":"2Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Job N.","family":"Doornberg","sequence":"additional","affiliation":[{"name":"1Department of Orthopaedic Surgery, Amsterdam UMC, University of Amsterdam, the Netherlands"},{"name":"3Department of Orthopaedic & Trauma Surgery, Flinders Medical Centre, Flinders University, Adelaide, SA, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"Machine Learning Consortium","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"416","published-online":{"date-parts":[[2020,10,26]]},"reference":[{"key":"2026070711284163300_bibr1-2058-5241.5.190092","first-page":"22","article-title":"Machine learning, predictive analytics, and clinical practice: can the past inform the present?","author":"Peterson","year":"2019","journal-title":"JAMA"},{"key":"2026070711284163300_bibr2-2058-5241.5.190092","doi-asserted-by":"crossref","first-page":"1498","DOI":"10.1056\/NEJM199606063342303","article-title":"Prediction of the need for intensive care in patients who come to emergency departments with acute chest pain","volume":"334","author":"Goldman","year":"1996","journal-title":"N Engl J Med"},{"key":"2026070711284163300_bibr3-2058-5241.5.190092","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1001\/jama.288.3.342","article-title":"Impact of a clinical decision rule on hospital triage of patients with suspected acute cardiac ischemia in the emergency department","volume":"288","author":"Reilly","year":"2002","journal-title":"JAMA"},{"key":"2026070711284163300_bibr4-2058-5241.5.190092","doi-asserted-by":"crossref","first-page":"e025925","DOI":"10.1136\/bmjopen-2018-025925","article-title":"Towards a decision support tool for intensive care discharge: machine learning algorithm development using electronic healthcare data from MIMIC-III and Bristol, UK","volume":"9","author":"McWilliams","year":"2019","journal-title":"BMJ Open"},{"key":"2026070711284163300_bibr5-2058-5241.5.190092","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1186\/s12916-019-1382-x","article-title":"Beyond the hype of big data and artificial intelligence: building foundations for knowledge and wisdom","volume":"17","author":"Car","year":"2019","journal-title":"BMC Med"},{"key":"2026070711284163300_bibr6-2058-5241.5.190092","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1148\/radiol.2018180694","article-title":"Mammographic breast density assessment using deep learning: clinical implementation","volume":"290","author":"Lehman","year":"2019","journal-title":"Radiology"},{"key":"2026070711284163300_bibr7-2058-5241.5.190092","unstructured":"Skeletal Oncology Research Group. 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