The DOI matches its Crossref record; 77 of the 77 references it cites were found in a registry or an index.
Artificial intelligence in orthopaedic surgery: exploring its applications, limitations, and future direction
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Figures
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Convolutional neural networks: an overview and application in radiology
Yamashita, R., Nishio, M., Do, R. K. G., & Togashi, K. (2018) · Insights into Imaging · doi:10.1007/s13244-018-0639-9 · Cited by 4,682
Artificial intelligence in surgery: promises and perils
Hashimoto, D. A., Rosman, G., Rus, D., & Meireles, O. R. (2018) · Annals of Surgery · doi:10.1097/sla.0000000000002693 · Cited by 1,468
Definition of Periprosthetic Joint Infection
Parvizi, J., & Gehrke, T. A. (2014) · The Journal of Arthroplasty · doi:10.1016/j.arth.2014.03.009 · Cited by 916
Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist.
Norgeot, B., Quer, G., Beaulieu-Jones, B. K., Torkamani, A., Dias, R., Gianfrancesco, M., Arnaout, R., Kohane, I. S., Saria, S., Topol, E., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-1041-y · Cited by 665
A standardized method for assessment of elbow function
Research Committee, A. S. A. E. S., Graham J.W King, M., Robin R Richards, M., Joseph D Zuckerman, M., Ralph Blasier, M., Charles Dillman, M., Richard J Friedman, M., Gary M Gartsman, M., Joseph P Iannotti, M., J.Patrick Murnahan, M., et al. (1999) · Journal of Shoulder and Elbow Surgery · doi:10.1016/s1058-2746(99)90159-3 · Cited by 286
Artificial Intelligence and Orthopaedics
Myers, T. G., Ramkumar, P. N., Ricciardi, B. F., Urish, K. L., Kipper, J., & Ketonis, C. (2020) · Journal of Bone and Joint Surgery · doi:10.2106/jbjs.19.01128 · Cited by 254
Leung, K., Zhang, B., Tan, J., Shen, Y., Geras, K. J., Babb, J. S., Cho, K., Chang, G. A., & Deniz, C. M. (2020) · Radiology · doi:10.1148/radiol.2020192091 · Cited by 251
The Digital Twin in Medicine: A Key to the Future of Healthcare?
Sun, T., He, X., Song, X., Shu, L., & Li, Z. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.907066 · Cited by 213
Ramkumar, P. N., Haeberle, H. S., Ramanathan, D., Cantrell, W. A., Navarro, S. M., Mont, M. A., Bloomfield, M. R., & Patterson, B. M. (2019) · The Journal of Arthroplasty · doi:10.1016/j.arth.2019.05.021 · Cited by 209
Langerhuizen, D. W. G., Janssen, S. J., Mallee, W. H., van den Bekerom, M. P. J., Ring, D., Kerkhoffs, G. M. M. J., Jaarsma, R. L., & Doornberg, J. N. (2019) · Clinical Orthopaedics and Related Research · doi:10.1097/corr.0000000000000848 · Cited by 197
Research works printed but not linked: 1
- Kim KM , Yefimova M , Lin FV , Jopling JK , Hansen EN . A Home-Recovery Surgical Care Model Using AI-Driven Measures of Activities of Daily Living . NEJM Catal . 2022 . 10.1056/CAT.22.0081 . https://catalyst.nejm.org/doi/full/10.1056/CAT.22.0081 ( date last accessed 23 June 2023 ). · DOI
Cited by
Sorted by citations
Artificial intelligence: revolutionizing robotic surgery: review
Iftikhar, M., Saqib, M., Zareen, M., & Mumtaz, H. (2024) · Annals of Medicine and Surgery · doi:10.1097/ms9.0000000000002426 · Cited by 161
ChatGPT and large language models in orthopedics: from education and surgery to research
Chatterjee, S., Bhattacharya, M., Pal, S., Lee, S., & Chakraborty, C. (2023) · Journal of Experimental Orthopaedics · doi:10.1186/s40634-023-00700-1 · Cited by 85
Prospective applications of bioactive materials in orthopedic therapies: A review
Liang, W., Zhou, C., Bai, J., Zhang, H., Long, H., Jiang, B., Wang, J., Huang, X., Zhang, H., & Zhao, J. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e36152 · Cited by 45
AI in Orthopedic Research: A Comprehensive Review
Mısır, A., & Yuce, A. (2025) · Journal of Orthopaedic Research® · doi:10.1002/jor.26109 · Cited by 46
Applications of artificial intelligence for adolescent idiopathic scoliosis: mapping the evidence
Goldman, S. N., Hui, A. T., Choi, S., Mbamalu, E., Tirabady, P., Eleswarapu, A. S., Gómez, J. A., Alvandi, L. M., & Fornari, E. D. (2024) · Spine Deformity · doi:10.1007/s43390-024-00940-w · Cited by 40
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 ›
Are you an author, or do you see a problem with this page? Request a review
