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Artificial intelligence in orthopaedic surgery: exploring its applications, limitations, and future direction
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The use of artificial intelligence (AI) is rapidly growing across many domains, of which the medical field is no exception. AI is an umbrella term defining the practical application of algorithms to generate useful output, without the need of human cognition. Owing to the expanding volume of patient information collected, known as 'big data', AI is showing promise as a useful tool in healthcare research and across all aspects of patient care pathways. Practical applications in orthopaedic surgery include: diagnostics, such as fracture recognition and tumour detection; predictive models of clinical and patient-reported outcome measures, such as calculating mortality rates and length of hospital stay; and real-time rehabilitation monitoring and surgical training. However, clinicians should remain cognizant of AI's limitations, as the development of robust reporting and validation frameworks is of paramount importance to prevent avoidable errors and biases. The aim of this review article is to provide a comprehensive understanding of AI and its subfields, as well as to delineate its existing clinical applications in trauma and orthopaedic surgery. Furthermore, this narrative review expands upon the limitations of AI and future direction.
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“Ethical concerns regarding data privacy, the potential for misinformation, and the need for regular content updates remain significant challenges for AI applications in orthopaedics [18].”
From Comparative Efficacy of ChatGPT and DeepSeek in Addressing Patient Queries on Gonarthrosis and Total Knee Arthroplasty (2025) · cites this paper as [18]
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Machine learning is better than surgeons at assessing unicompartmental knee replacement radiographs
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Humphreys, S. C., Block, J. E., Sivaganesan, A., Nel, L. J., Peterman, M., & Hodges, S. D. (2025) · Expert Review of Medical Devices · doi:10.1080/17434440.2025.2484252 · Cited by 3
Hiemstra, L. A. (2024) · Arthroscopy The Journal of Arthroscopic and Related Surgery · doi:10.1016/j.arthro.2024.09.030 · Cited by 3
Artificial intelligence in traumatology and orthopedics. Reality, fantasy or false hopes?
Середа, А. П., Аббасович, Д. А., & Cherny, A. A. (2024) · Traumatology and Orthopedics of Russia · doi:10.17816/2311-2905-17468 · Cited by 2
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Metadata
- Authors
- Lisacek-Kiosoglous, A. B., Powling, A. S., Fontalis, A., Gabr, A., Mazomenos, E., & Haddad, F. S.
- Affiliations
- University College Hospital (GB)
- Wellcome / EPSRC Centre for Interventional and Surgical Sciences (GB)
- Corresponding author
- Fontalis, Andreas
- ORCID
- Venue
- Bone & Joint Research
- Published
- 2023-07-10
- Volume
- 12
- Issue
- 7
- Pages
- 447-454
- ISSN
- 2046-3758 (ISSN-L)
- PMC
- PMC10329876
- OpenAlex
- W4383683017
- Cited by
- 184
- References
- 77
- Citations by year
- 2026: 56 · 2025: 75 · 2024: 43 · 2023: 10
- FWCI
- 7.35
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Bone and Joint Research (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Wellcome Trust (203145/Z/16/Z)
- Alexander S. Onassis Public Benefit Foundation
- Wellcome / EPSRC Centre for Interventional and Surgical Sciences (203145Z/16/Z)
- Engineering and Physical Sciences Research Council (203145/Z/16/Z)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Keywords
- artificial intelligence, orthopedic surgery, trauma surgery, fracture detection, cancer detection, prediction model, mortality prediction, length of hospital stay, patient-reported outcomes, rehabilitation monitoring, surgical training, algorithmic bias, validation framework, big data
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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