AIMC Topic: United States Food and Drug Administration

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FDA-approved machine learning algorithms in neuroradiology: A systematic review of the current evidence for approval.

Artificial intelligence in medicine
Over the past decade, machine learning (ML) and artificial intelligence (AI) have become increasingly prevalent in the medical field. In the United States, the Food and Drug Administration (FDA) is responsible for regulating AI algorithms as "medical...

The regulatory environment for artificial intelligence-enabled devices in the United States.

Seminars in vascular surgery
The regulatory environment in the United States has not kept pace with the rapidly developing market for artificial intelligence (AI)-enabled devices. The number of AI-enabled devices has increased year after year. All of these devices are registered...

US regulatory considerations for low field magnetic resonance imaging systems.

Magma (New York, N.Y.)
Although there has been a resurgence of interest in low field magnetic resonance imaging (MRI) systems in recent years, low field MRI is not a new concept. FDA has a long history of evaluating the safety and effectiveness of MRI systems encompassing ...

Bayesian Statistics for Medical Devices: Progress Since 2010.

Therapeutic innovation & regulatory science
The use of Bayesian statistics to support regulatory evaluation of medical devices began in the late 1990s. We review the literature, focusing on recent developments of Bayesian methods, including hierarchical modeling of studies and subgroups, borro...

Trends in clinical validation and usage of US Food and Drug Administration-cleared artificial intelligence algorithms for medical imaging.

Clinical radiology
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 strategy.

The current status and future of FDA-approved artificial intelligence tools in chest radiology in the United States.

Clinical radiology
Artificial intelligence (AI) is becoming more widespread within radiology. Capabilities that AI algorithms currently provide include detection, segmentation, classification, and quantification of pathological findings. Artificial intelligence softwar...

Utilizing Deep Learning for Detecting Adverse Drug Events in Structured and Unstructured Regulatory Drug Data Sets.

Pharmaceutical medicine
BACKGROUND: The US Food and Drug Administration (FDA) collects and retains several data sets on post-market drugs and associated adverse events (AEs). The FDA Adverse Event Reporting System (FAERS) contains millions of AE reports submitted by the pub...

Discovery of moiety preference by Shapley value in protein kinase family using random forest models.

BMC bioinformatics
BACKGROUND: Human protein kinases play important roles in cancers, are highly co-regulated by kinase families rather than a single kinase, and complementarily regulate signaling pathways. Even though there are > 100,000 protein kinase inhibitors, onl...

The current status of breakthrough devices designation in the United States and innovative medical devices designation in Korea for digital health software.

Expert review of medical devices
INTRODUCTION: Artificial Intelligence (AI) is becoming increasingly utilized in the medical device industry as it can address unmet demands in clinical sites and provide more patient treatment options. This study aims to analyze the FDA's Breakthroug...

Advancing pharmacy and healthcare with virtual digital technologies.

Advanced drug delivery reviews
Digitalisation of the healthcare sector promises to revolutionise patient healthcare globally. From the different technologies, virtual tools including artificial intelligence, blockchain, virtual, and augmented reality, to name but a few, are provid...