A Primer on Radiomics for the Practicing Orthopaedic Surgeon.

Authors

William R Aibinder, Josie Elwell, Hamidreza Rajabzadeh-Oghaz, Bradley S Schoch

Published in

Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews. Volume 10. Issue 11. Nov 01, 2026. Epub Nov 02, 2026.

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.

PMID:
42970412
Bibliographic data and abstract were imported from PubMed on 03 Nov 2026.

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