Artificial Intelligence in Radiology: Opportunities and Challenges
Artificial intelligence’s (AI) emergence in radiology elicits both excitement and uncertainty. AI holds promise for improving radiology with regards to clinical practice, education, and research opportunities. Yet, AI systems are trained on select datasets that can contain bias and inaccuracies. Rad...
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| Veröffentlicht in: | Seminars in ultrasound, CT, and MRI CT, and MRI, 2024-04, Vol.45 (2), p.152-160 |
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| container_title | Seminars in ultrasound, CT, and MRI |
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| creator | Flory, Marta N. Napel, Sandy Tsai, Emily B. |
| description | Artificial intelligence’s (AI) emergence in radiology elicits both excitement and uncertainty. AI holds promise for improving radiology with regards to clinical practice, education, and research opportunities. Yet, AI systems are trained on select datasets that can contain bias and inaccuracies. Radiologists must understand these limitations and engage with AI developers at every step of the process – from algorithm initiation and design to development and implementation – to maximize benefit and minimize harm that can be enabled by this technology. |
| doi_str_mv | 10.1053/j.sult.2024.02.004 |
| format | Article |
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| fulltext | fulltext |
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| ispartof | Seminars in ultrasound, CT, and MRI, 2024-04, Vol.45 (2), p.152-160 |
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| language | eng |
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| source | MEDLINE; Elsevier ScienceDirect Journals Complete |
| subjects | Algorithms Artificial Intelligence Diagnostic Imaging - methods Humans Radiology - methods |
| title | Artificial Intelligence in Radiology: Opportunities and Challenges |
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