Review of Artificial Intelligence Training Tools and Courses for Radiologists
| Autor: | Robert C. Thomas, Xuan V. Nguyen, Joseph S. Fotos, Nikita Consul, Lonie R. Salkowski, William F. Auffermann, Ichiro Ikuta, Atul Agarwal, Shafik N. Wassef, Hao S. Lo, Linda C. Kelahan, Scott J. Adams, Jessica M. Sin, Anup K. Bhattacharya, Christine Lin, Michael L. Richardson |
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| Rok vydání: | 2021 |
| Předmět: |
Computer science
Task force business.industry Deep learning Imaging chain 030218 nuclear medicine & medical imaging Radiography 03 medical and health sciences 0302 clinical medicine Artificial Intelligence 030220 oncology & carcinogenesis Radiologists Humans Radiology Nuclear Medicine and imaging Artificial intelligence Report generation Radiology business Ai systems |
| Zdroj: | Academic Radiology. 28:1238-1252 |
| ISSN: | 1076-6332 |
| DOI: | 10.1016/j.acra.2020.12.026 |
| Popis: | Artificial intelligence (AI) systems play an increasingly important role in all parts of the imaging chain, from image creation to image interpretation to report generation. In order to responsibly manage radiology AI systems and make informed purchase decisions about them, radiologists must understand the underlying principles of AI. Our task force was formed by the Radiology Research Alliance (RRA) of the Association of University Radiologists to identify and summarize a curated list of current educational materials available for radiologists. |
| Databáze: | OpenAIRE |
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