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Framework and metrics for the clinical use and implementation of artificial intelligence algorithms into endoscopy practice: recommendations from the American Society for Gastrointestinal Endoscopy Artificial Intelligence Task Force.
Parasa, Sravanthi; Repici, Alessandro; Berzin, Tyler; Leggett, Cadman; Gross, Seth A; Sharma, Prateek.
Afiliación
  • Parasa S; Department of Gastroenterology, Swedish Medical Center, Seattle, Washington, USA.
  • Repici A; Digestive Endoscopy Department, Humanitas Research Hospital & University, Milano, Italy.
  • Berzin T; Division of Gastroenterology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.
  • Leggett C; Department of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA.
  • Gross SA; Division of Gastroenterology and Hepatology, NYU Langone, New York, New York, USA.
  • Sharma P; Gastroenterology, Hepatology & Motility Division, University of Kansas Medical Center, Kansas City, Kansas, USA.
Gastrointest Endosc ; 97(5): 815-824.e1, 2023 05.
Article en En | MEDLINE | ID: mdl-36764886
ABSTRACT
In the past few years, we have seen a surge in the development of relevant artificial intelligence (AI) algorithms addressing a variety of needs in GI endoscopy. To accept AI algorithms into clinical practice, their effectiveness, clinical value, and reliability need to be rigorously assessed. In this article, we provide a guiding framework for all stakeholders in the endoscopy AI ecosystem regarding the standards, metrics, and evaluation methods for emerging and existing AI applications to aid in their clinical adoption and implementation. We also provide guidance and best practices for evaluation of AI technologies as they mature in the endoscopy space. Note, this is a living document; periodic updates will be published as progress is made and applications evolve in the field of AI in endoscopy.
Asunto(s)

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Benchmarking Tipo de estudio: Guideline Límite: Humans Idioma: En Revista: Gastrointest Endosc Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Benchmarking Tipo de estudio: Guideline Límite: Humans Idioma: En Revista: Gastrointest Endosc Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos