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Critical Appraisal of Artificial Intelligence-Enabled Imaging Tools Using the Levels of Evidence System.
Pham, N; Hill, V; Rauschecker, A; Lui, Y; Niogi, S; Fillipi, C G; Chang, P; Zaharchuk, G; Wintermark, M.
Afiliação
  • Pham N; From the Department of Radiology (N.P., G.Z.), Stanford School of Medicine, Palo Alto, California pham.nancy@gmail.com.
  • Hill V; Department of Radiology (V.H.), Northwestern University Feinberg School of Medicine, Chicago, Illinois.
  • Rauschecker A; Department of Radiology (A.R.), University of California, San Francisco, San Francisco, California.
  • Lui Y; Department of Radiology (Y.L.), NYU Grossman School of Medicine, New York, New York.
  • Niogi S; Department of Radiology (S.N.), Weill Cornell Medicine, New York, New York.
  • Fillipi CG; Department of Radiology (C.G.F.), Tufts University School of Medicine, Boston, Massachusetts.
  • Chang P; Department of Radiology (P.C.), University of California, Irvine, Irvine, California.
  • Zaharchuk G; From the Department of Radiology (N.P., G.Z.), Stanford School of Medicine, Palo Alto, California.
  • Wintermark M; Department of Neuroradiology (M.W.), The University of Texas MD Anderson Cancer Center, Houston, Texas.
AJNR Am J Neuroradiol ; 44(5): E21-E28, 2023 05.
Article em En | MEDLINE | ID: mdl-37080722
ABSTRACT
Clinical adoption of an artificial intelligence-enabled imaging tool requires critical appraisal of its life cycle from development to implementation by using a systematic, standardized, and objective approach that can verify both its technical and clinical efficacy. Toward this concerted effort, the ASFNR/ASNR Artificial Intelligence Workshop Technology Working Group is proposing a hierarchal evaluation system based on the quality, type, and amount of scientific evidence that the artificial intelligence-enabled tool can demonstrate for each component of its life cycle. The current proposal is modeled after the levels of evidence in medicine, with the uppermost level of the hierarchy showing the strongest evidence for potential impact on patient care and health care outcomes. The intended goal of establishing an evidence-based evaluation system is to encourage transparency, foster an understanding of the creation of artificial intelligence tools and the artificial intelligence decision-making process, and to report the relevant data on the efficacy of artificial intelligence tools that are developed. The proposed system is an essential step in working toward a more formalized, clinically validated, and regulated framework for the safe and effective deployment of artificial intelligence imaging applications that will be used in clinical practice.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Diagnóstico por Imagem Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Revista: AJNR Am J Neuroradiol Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Diagnóstico por Imagem Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Revista: AJNR Am J Neuroradiol Ano de publicação: 2023 Tipo de documento: Article