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DC Field | Value | Language |
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dc.contributor.author | Kallianos, K, | - |
dc.contributor.author | Abuya, J | - |
dc.date.accessioned | 2021-03-25T07:36:12Z | - |
dc.date.available | 2021-03-25T07:36:12Z | - |
dc.date.issued | 2019-01 | - |
dc.identifier.uri | https://www.researchgate.net/publication/330690552_ | - |
dc.identifier.uri | http://ir.mu.ac.ke:8080/jspui/handle/123456789/4351 | - |
dc.description.abstract | Due to recent advances in artificial intelligence, there is renewed interest in automating interpretation of imaging tests. Chest radiographs are particularly interesting due to many factors: relatively inexpensive equipment, importance to public health, commonly performed throughout the world, and deceptively complex taking years to master. This article presents a brief introduction to artificial intelligence, reviews the progress to date in chest radiograph interpretation, and provides a snapshot of the available datasets and algorithms available to chest radiograph researchers. Finally, the limitations of artificial intelligence with respect to interpretation of imaging studies are discussed | en_US |
dc.language.iso | en | en_US |
dc.publisher | EPub | en_US |
dc.subject | Artificial intelligence | en_US |
dc.subject | chest radiograph interpretation | en_US |
dc.title | How far have we come? Artificial intelligence for chest radiograph interpretation | en_US |
dc.type | Article | en_US |
Appears in Collections: | School of Medicine |
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