AIMC Topic: United States

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Artificial intelligence and real-world data for drug and food safety - A regulatory science perspective.

Regulatory toxicology and pharmacology : RTP
In 2013, the Global Coalition for Regulatory Science Research (GCRSR) was established with members from over ten countries (www.gcrsr.net). One of the main objectives of GCRSR is to facilitate communication among global regulators on the rise of new ...

Neural network based integration of assays to assess pathogenic potential.

Scientific reports
Limited data significantly hinders our capability of biothreat assessment of novel bacterial strains. Integration of data from additional sources that can provide context about the strain can address this challenge. Datasets from different sources, h...

Disparities in access to robotic technology and perioperative outcomes among patients treated with radical prostatectomy.

Journal of surgical oncology
BACKGROUND: Most radical prostatectomies are completed with robotic assistance. While studies have previously evaluated perioperative outcomes of robot-assisted radical prostatectomy (RARP), this study investigates disparities in access and clinical ...

Surveying Public Perceptions of Artificial Intelligence in Health Care in the United States: Systematic Review.

Journal of medical Internet research
BACKGROUND: This paper reviews nationally representative public opinion surveys on artificial intelligence (AI) in the United States, with a focus on areas related to health care. The potential health applications of AI continue to gain attention owi...

Deep learning mapping of surface MDA8 ozone: The impact of predictor variables on ozone levels over the contiguous United States.

Environmental pollution (Barking, Essex : 1987)
The limited number of ozone monitoring stations imposes uncertainty in various applications, calling for accurate approaches to capturing ozone values in all regions, particularly those with no in-situ measurements. This study uses deep learning (DL)...

Application of a developed triple-classification machine learning model for carcinogenic prediction of hazardous organic chemicals to the US, EU, and WHO based on Chinese database.

Ecotoxicology and environmental safety
Cancer, the second largest human disease, has become a major public health problem. The prediction of chemicals' carcinogenicity before their synthesis is crucial. In this paper, seven machine learning algorithms (i.e., Random Forest (RF), Logistic R...

A survey of ASER members on artificial intelligence in emergency radiology: trends, perceptions, and expectations.

Emergency radiology
PURPOSE: There is a growing body of diagnostic performance studies for emergency radiology-related artificial intelligence/machine learning (AI/ML) tools; however, little is known about user preferences, concerns, experiences, expectations, and the d...

On the Best Way to Cluster NCI-60 Molecules.

Biomolecules
Machine learning-based models have been widely used in the early drug-design pipeline. To validate these models, cross-validation strategies have been employed, including those using clustering of molecules in terms of their chemical structures. Howe...

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...

An Unsupervised Machine Learning Approach to Evaluating the Association of Symptom Clusters With Adverse Outcomes Among Older Adults With Advanced Cancer: A Secondary Analysis of a Randomized Clinical Trial.

JAMA network open
IMPORTANCE: Older adults with advanced cancer who have high pretreatment symptom severity often experience adverse events during cancer treatments. Unsupervised machine learning may help stratify patients into different risk groups.