M Khunte, A Chae, R Wang, R Jain, Y Sun, JR Sollee… - Clinical radiology, 2023 - Elsevier
AIM To examine the current landscape of US Food and Drug Administration (FDA)-approved artificial intelligence (AI) medical imaging devices and identify trends in clinical validation …
S Ebrahimian, MK Kalra, S Agarwal, BC Bizzo… - Academic radiology, 2022 - Elsevier
Rationale and Objectives To assess key trends, strengths, and gaps in validation studies of the Food and Drug Administration (FDA)-regulated imaging-based artificial …
As applications of AI in medicine continue to expand, there is an increasing focus on integration into clinical practice. An underappreciated aspect of this clinical translation is …
Abstract Background Intended use statements (IUSs) are mandatory to obtain regulatory clearance for artificial intelligence (AI)-based medical devices in the European Union. In …
G Shih, AE Flanders - American Journal of Roentgenology, 2024 - Am Roentgen Ray Soc
The investigation explores discrepancies in how vendors represent artificial intelligence or machine learning (AI/ML) capabilities of FDA-cleared products by comparing marketing …
MD Lin - Academic radiology, 2022 - academicradiology.org
There has been an exponential increase in the number of publications and work pertaining to applications of artificial intelligence (AI) and machine learning (ML) in medicine and in …
The potential applications of artificial intelligence and machine learning (AI/ML) in medicine are progressing rapidly. AI is a broad term that refers to the intelligence of computer and …
Purpose To introduce developers to medical device regulatory processes and data considerations in artificial intelligence and machine learning (AI/ML) device submissions …
A Tariq, S Purkayastha, GP Padmanaban… - Journal of the American …, 2020 - Elsevier
Purpose Despite tremendous gains from deep learning and the promise of artificial intelligence (AI) in medicine to improve diagnosis and save costs, there exists a large …