Artificial intelligence in planned orthopaedic care
The integration of artificial intelligence (AI) into orthopaedic care has gained considerable interest in recent years, evidenced by the growing body of literature boasting wide-ranging applications across the perioperative setting. This includes automated diagnostic imaging, clinical decision-makin...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2024-01-01
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| Series: | SICOT-J |
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| Online Access: | https://www.sicot-j.org/articles/sicotj/full_html/2024/01/sicotj240098/sicotj240098.html |
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| author | Georgiakakis Elena Chiara Thalia Khan Akib Majed Logishetty Kartik Sarraf Khaled Maher |
| author_facet | Georgiakakis Elena Chiara Thalia Khan Akib Majed Logishetty Kartik Sarraf Khaled Maher |
| author_sort | Georgiakakis Elena Chiara Thalia |
| collection | DOAJ |
| description | The integration of artificial intelligence (AI) into orthopaedic care has gained considerable interest in recent years, evidenced by the growing body of literature boasting wide-ranging applications across the perioperative setting. This includes automated diagnostic imaging, clinical decision-making tools, optimisation of implant design, robotic surgery, and remote patient monitoring. Collectively, these advances propose to enhance patient care and improve system efficiency. Musculoskeletal pathologies represent the most significant contributor to global disability, with roughly 1.71 billion people afflicted, leading to an increasing volume of patients awaiting planned orthopaedic surgeries. This has exerted a considerable strain on healthcare systems globally, compounded by both the COVID-19 pandemic and the effects of an ageing population. Subsequently, patients face prolonged waiting times for surgery, with further deterioration and potentially poorer outcomes as a result. Furthermore, incorporating AI technologies into clinical practice could provide a means of addressing current and future service demands. This review aims to present a clear overview of AI applications across preoperative, intraoperative, and postoperative stages to elucidate its potential to transform planned orthopaedic care. |
| format | Article |
| id | doaj-art-5b85addd283e4fda8a99005e8a337d85 |
| institution | OA Journals |
| issn | 2426-8887 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | SICOT-J |
| spelling | doaj-art-5b85addd283e4fda8a99005e8a337d852025-08-20T02:19:12ZengEDP SciencesSICOT-J2426-88872024-01-01104910.1051/sicotj/2024044sicotj240098Artificial intelligence in planned orthopaedic careGeorgiakakis Elena Chiara Thalia0https://orcid.org/0009-0005-0702-291XKhan Akib Majed1https://orcid.org/0009-0002-4601-7038Logishetty Kartik2https://orcid.org/0000-0002-0469-9539Sarraf Khaled Maher3Cambridge University Hospitals NHS Foundation TrustImperial College Healthcare NHS TrustImperial College Healthcare NHS TrustImperial College Healthcare NHS TrustThe integration of artificial intelligence (AI) into orthopaedic care has gained considerable interest in recent years, evidenced by the growing body of literature boasting wide-ranging applications across the perioperative setting. This includes automated diagnostic imaging, clinical decision-making tools, optimisation of implant design, robotic surgery, and remote patient monitoring. Collectively, these advances propose to enhance patient care and improve system efficiency. Musculoskeletal pathologies represent the most significant contributor to global disability, with roughly 1.71 billion people afflicted, leading to an increasing volume of patients awaiting planned orthopaedic surgeries. This has exerted a considerable strain on healthcare systems globally, compounded by both the COVID-19 pandemic and the effects of an ageing population. Subsequently, patients face prolonged waiting times for surgery, with further deterioration and potentially poorer outcomes as a result. Furthermore, incorporating AI technologies into clinical practice could provide a means of addressing current and future service demands. This review aims to present a clear overview of AI applications across preoperative, intraoperative, and postoperative stages to elucidate its potential to transform planned orthopaedic care.https://www.sicot-j.org/articles/sicotj/full_html/2024/01/sicotj240098/sicotj240098.htmlartificial intelligencemachine learningdeep learningplanned orthopaedic care |
| spellingShingle | Georgiakakis Elena Chiara Thalia Khan Akib Majed Logishetty Kartik Sarraf Khaled Maher Artificial intelligence in planned orthopaedic care SICOT-J artificial intelligence machine learning deep learning planned orthopaedic care |
| title | Artificial intelligence in planned orthopaedic care |
| title_full | Artificial intelligence in planned orthopaedic care |
| title_fullStr | Artificial intelligence in planned orthopaedic care |
| title_full_unstemmed | Artificial intelligence in planned orthopaedic care |
| title_short | Artificial intelligence in planned orthopaedic care |
| title_sort | artificial intelligence in planned orthopaedic care |
| topic | artificial intelligence machine learning deep learning planned orthopaedic care |
| url | https://www.sicot-j.org/articles/sicotj/full_html/2024/01/sicotj240098/sicotj240098.html |
| work_keys_str_mv | AT georgiakakiselenachiarathalia artificialintelligenceinplannedorthopaediccare AT khanakibmajed artificialintelligenceinplannedorthopaediccare AT logishettykartik artificialintelligenceinplannedorthopaediccare AT sarrafkhaledmaher artificialintelligenceinplannedorthopaediccare |