How far have we come? Artificial intelligence for chest radiograph interpretation.

By K. Kallianos, John T Mongan, Sameer Kiran Antani, Travis Henry, Andrew G. Taylor, J. Abuya, Marc D. Kohli. 2019

145 citations recorded by the source corpus.

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.

Subfields: Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

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In Pulmonary and Respiratory Medicine

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI (2019, background): “They provide summaries of previous works [21] including subfieldspecific reviews such as [25] for chest radiograph and sentiment analysis in medicine [160], or at least set aside a section”
Hybrid deep learning for detecting lung diseases from X-ray images (2020, unknown): “Furthermore, the work [16] describes this issue besides organizing a novel 108,948 front outlook database known as ChestX-ray8 where the 32,717 X-ray images are of unique patients.”
Identifying pneumonia in chest X-rays: A deep learning approach (2019, background): “Kallianos et al. [17] presented a state of art review stating the importance of artiﬁcial intelligence in chest X-ray image classiﬁcation and analysis.”
Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray (2020, methodology): “) are using Artificial Intelligence (AI) based solutions [7-10].”
Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond (2021, unknown): “More specifically, specific analyses have been investigated, e.g., chest radiography [128], emotion analysis in medicine [129], COVID-19 detection and classification [43], and the research encourages understanding of the importance of interpretability in the medical field [130].”