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Artificial Intelligence in Breast Imaging: Challenges of Integration Into Clinical Practice.
Ozcan, B Bersu; Patel, Bhavika K; Banerjee, Imon; Dogan, Basak E.
Afiliação
  • Ozcan BB; The University of Texas Southwestern Medical Center, Department of Radiology, Dallas, TX, USA.
  • Patel BK; Mayo Clinic, Department of Radiology, Scottsdale, AZ, USA.
  • Banerjee I; Mayo Clinic, Department of Radiology, Scottsdale, AZ, USA.
  • Dogan BE; The University of Texas Southwestern Medical Center, Department of Radiology, Dallas, TX, USA.
J Breast Imaging ; 5(3): 248-257, 2023 May 22.
Article em En | MEDLINE | ID: mdl-38416888
ABSTRACT
Artificial intelligence (AI) in breast imaging is a rapidly developing field with promising results. Despite the large number of recent publications in this field, unanswered questions have led to limited implementation of AI into daily clinical practice for breast radiologists. This paper provides an overview of the key limitations of AI in breast imaging including, but not limited to, limited numbers of FDA-approved algorithms and annotated data sets with histologic ground truth; concerns surrounding data privacy, security, algorithm transparency, and bias; and ethical issues. Ultimately, the successful implementation of AI into clinical care will require thoughtful action to address these challenges, transparency, and sharing of AI implementation workflows, limitations, and performance metrics within the breast imaging community and other end-users.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Diagnóstico por Imagem Limite: Humans Idioma: En Revista: J Breast Imaging Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Diagnóstico por Imagem Limite: Humans Idioma: En Revista: J Breast Imaging Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos