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Artificial Intelligence in Detection of Distal Radius Fractures in Adults: A Scoping Review

Year: 2026

Type: article

Abstract: Artificial intelligence has the potential to assist in the interpretation of radiographs. The distal radius is one of the most commonly fractured bones, and accurate and timely diagnosis is required for proper treatment. This review aims to summarize the current literature on the use of artificial intelligence, particularly convolutional neural networks, in diagnosing distal radius fractures. The results indicate that artificial intelligence has shown increasingly improved performance with image interpretation over time, often on par with specialized physicians. However, these models still struggle with complex fractures.

Source: Hand

DOI: 10.1177/15589447261433067

Authors: Sofia Griff, Lauren Neal, Juliet Chung, Benjamin R. Graves

Institutions: Wake Forest University Health Sciences, Wake Forest University, Atrium Health Wake Forest Baptist

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W7153603305

FWCI:

Cites: 33

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