How far have we come? Artificial intelligence for chest radiograph interpretation. Issue 5 (May 2019)

• Record Type: Journal Article
• Authors: Kallianos, K.; Mongan, J.; Antani, S.; Henry, T.; Taylor, A.; Abuya, J.; Kohli, M.
• Is Part Of: Clinical radiology. Volume 74:Issue 5(2019)
• Page Start: 338
• Page End: 345
• Publication Date: 2019-05
• Subjects: Medical radiology -- Periodicals; Radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiology -- Periodicals; Societies, Medical -- Periodicals; Medical radiology; Radiotherapy; Electronic journals; Periodicals; 616.0757
• DOI: 10.1016/j.crad.2018.12.015
• Languages: English
• ISSNs: 0009-9260

Abstract

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.