Please use this identifier to cite or link to this item: http://ir.mu.ac.ke:8080/jspui/handle/123456789/4351
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dc.contributor.authorKallianos, K,-
dc.contributor.authorAbuya, J-
dc.date.accessioned2021-03-25T07:36:12Z-
dc.date.available2021-03-25T07:36:12Z-
dc.date.issued2019-01-
dc.identifier.urihttps://www.researchgate.net/publication/330690552_-
dc.identifier.urihttp://ir.mu.ac.ke:8080/jspui/handle/123456789/4351-
dc.description.abstractDue 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 discusseden_US
dc.language.isoenen_US
dc.publisherEPuben_US
dc.subjectArtificial intelligenceen_US
dc.subjectchest radiograph interpretationen_US
dc.titleHow far have we come? Artificial intelligence for chest radiograph interpretationen_US
dc.typeArticleen_US
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