Ready for testing artificial intelligence in radiology clinical practice: We would do well to be in the front line leveraging their strengths but also highlighting today weaknesses.
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| Title: | Ready for testing artificial intelligence in radiology clinical practice: We would do well to be in the front line leveraging their strengths but also highlighting today weaknesses. |
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| Authors: | Bender, Benjamin1 (AUTHOR) Benjamin.bender@med.uni-tuebingen.de |
| Source: | European Radiology. Feb2024, Vol. 34 Issue 2, p808-809. 2p. |
| Subjects: | Radiology, Artificial intelligence, Intelligence tests, Artificial neural networks |
| Abstract: | The article discusses the potential use of artificial intelligence (AI) in radiology practice. It explains that deep learning, a specific strategy of machine learning, has shown promising results in tasks related to radiology. The article highlights the importance of training AI models with large, well-labeled datasets and discusses a study that compared the performance of radiologists with and without the assistance of an AI model. The study found that the AI model performed better than the average radiologist in detecting certain findings, but there were still limitations and risks associated with using AI tools. The article concludes by suggesting that AI tools could improve the quality of radiological reports and calls for further research to validate their benefits. [Extracted from the article] |
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| Database: | Engineering Source |
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| Abstract: | The article discusses the potential use of artificial intelligence (AI) in radiology practice. It explains that deep learning, a specific strategy of machine learning, has shown promising results in tasks related to radiology. The article highlights the importance of training AI models with large, well-labeled datasets and discusses a study that compared the performance of radiologists with and without the assistance of an AI model. The study found that the AI model performed better than the average radiologist in detecting certain findings, but there were still limitations and risks associated with using AI tools. The article concludes by suggesting that AI tools could improve the quality of radiological reports and calls for further research to validate their benefits. [Extracted from the article] |
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| ISSN: | 09387994 |
| DOI: | 10.1007/s00330-023-10240-y |
