| Autor: |
John R, Zech, Samantha M, Santomartino, Paul H, Yi |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
AJR. American journal of roentgenology. 219(6) |
| ISSN: |
1546-3141 |
| Popis: |
Fractures are common injuries that can be difficult to diagnose, with missed fractures accounting for most misdiagnoses in the emergency department. Artificial intelligence (AI) and, specifically, deep learning have shown a strong ability to accurately detect fractures and augment the performance of radiologists in proof-of-concept research settings. Although the number of real-world AI products available for clinical use continues to increase, guidance for practicing radiologists in the adoption of this new technology is limited. This review describes how AI and deep learning algorithms can help radiologists to better diagnose fractures. The article also provides an overview of commercially available U.S. FDA-cleared AI tools for fracture detection as well as considerations for the clinical adoption of these tools by radiology practices. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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