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How far have we come? Artificial intelligence for chest radiograph interpretation.
Kallianos, K; Mongan, J; Antani, S; Henry, T; Taylor, A; Abuya, J; Kohli, M.
Afiliação
  • Kallianos K; Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA.
  • Mongan J; Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA.
  • Antani S; National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
  • Henry T; Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA.
  • Taylor A; Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA.
  • Abuya J; Department of Radiology, School of Medicine, College of Health Sciences, Moi University, Eldoret, Kenya.
  • Kohli M; Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA. Electronic address: marc.kohli@ucsf.edu.
Clin Radiol ; 74(5): 338-345, 2019 05.
Article em En | MEDLINE | ID: mdl-30704666
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.
Assuntos

Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Inteligência Artificial / Radiografia Torácica Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Revista: Clin Radiol Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Inteligência Artificial / Radiografia Torácica Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Revista: Clin Radiol Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Estados Unidos