A Primer on Radiomics for the Practicing Orthopaedic Surgeon
- 1From the Department of Orthopaedic Surgery (Dr. Aibinder), University of Michigan, Ann Arbor, MI, the Advita Ortho (Dr. Elwell, Dr. Rajabzadeh-Oghaz), Gainesville, FL, and Department of Orthopaedic Surgery (Dr. Schoch), Mayo Clinic Florida, Jacksonville, FL.
Abstract
Artificial intelligence (AI) is permeating many aspects of daily life. Medicine, and specifically orthopaedic surgery, is being influenced by the rapid growth and success of AI in serving as an adjunct to medical expertise. AI includes many nested subfields and applications such as computer vision, language models, and predictive analytics. Using standard machine learning methods, clinical decision support tools are being developed for clinicians to provide evidence-based, individualized care. Some models predict clinical and functional outcomes after a procedure, whereas others may incorporate administrative metrics to predict factors, such as length of stay, return to the emergency department, and complications. The application of AI to medical imaging focuses on the extraction of a large amount of quantitative data in the form of predefined features from clinical images that humans cannot perceive, a field known as radiomics. There has been recent interest, particularly in oncology, to apply radiomics to aid in diagnosis and prognostication of outcomes. Given the robust growth of radiomics literature, and the anticipated application in orthopaedic surgery, the purpose of this review article is to educate the orthopaedic surgery community on the practical importance and the basics of radiomics.
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BibTeX
@article{aibinder2026primer,
title = {{A Primer on Radiomics for the Practicing Orthopaedic Surgeon}},
author = {William R. Aibinder and Josie Elwell and Hamidreza Rajabzadeh-Oghaz and Bradley S. Schoch},
journal = {Journal of the American Academy of Orthopaedic Surgeons. Global research \& reviews},
year = {2026},
month = nov,
doi = {10.5435/jaaosglobal-d-25-00367},
url = {https://europepmc.org/article/PMC/PMC13722504},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - A Primer on Radiomics for the Practicing Orthopaedic Surgeon
AU - William R. Aibinder
AU - Josie Elwell
AU - Hamidreza Rajabzadeh-Oghaz
AU - Bradley S. Schoch
PY - 2026
DA - 2026/11/01
JO - Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews
DO - 10.5435/jaaosglobal-d-25-00367
UR - https://europepmc.org/article/PMC/PMC13722504
AB - Artificial intelligence (AI) is permeating many aspects of daily life. Medicine, and specifically orthopaedic surgery, is being influenced by the rapid growth and success of AI in serving as an adjunct to medical expertise. AI includes many nested subfields and applications such as computer vision, language models, and predictive analytics. Using standard machine learning methods, clinical decision support tools are being developed for clinicians to provide evidence-based, individualized care. Some models predict clinical and functional outcomes after a procedure, whereas others may incorporate administrative metrics to predict factors, such as length of stay, return to the emergency department, and complications. The application of AI to medical imaging focuses on the extraction of a large amount of quantitative data in the form of predefined features from clinical images that humans cannot perceive, a field known as radiomics. There has been recent interest, particularly in oncology, to apply radiomics to aid in diagnosis and prognostication of outcomes. Given the robust growth of radiomics literature, and the anticipated application in orthopaedic surgery, the purpose of this review article is to educate the orthopaedic surgery community on the practical importance and the basics of radiomics.
SN - 2474-7661
ER -