Étude et rapport

FDA-regulated AI Algorithms: Trends, Strengths, and Gaps of Validation Studies

GRATUIT

Auteur(s) :

Shadi Ebrahimian, Mannudeep K.Kalra, Sheela Agarwal, Bernardo C Bizzo, Mona Elkholy, Christoph Wald, Bibb Allen, Keith J.Dreyer

Éditeur(s) :

SCIENCE DIRECT

Date de publication :27/12/2021

1 pages

EN BREF ...

"We audited publicly available details of regulated AI/ML algorithms in imaging from 2008 until April 2021. We reviewed 127 regulated software (118 AI/ML) to classify information related to their parent company, subspecialty, body area and specific anatomy type, imaging modality, date of FDA clearance, indications for use, target pathology (such as trauma) and findings (such as fracture), technique (CAD triage, CAD detection and/or characterization, CAD acquisition or improvement, and image processing/quantification), product performance, presence, type, strength and availability of clinical validation data. Pertaining to validation data, where available, we recorded the number of patients or studies included, sensitivity, specificity, accuracy, and/or receiver operating characteristic area under the curve, along with information on ground-truthing of use-cases. Data were analyzed with pivot tables and charts for descriptive statistics and trends." En bref issu de l'étude.

Rédacteur(s) de la fiche : Beesens TEAM


Introductio

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Conclusio

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