AIMC Topic: Artificial Intelligence

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Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery.

Molecular diversity
The accumulation of massive data in the plethora of Cheminformatics databases has made the role of big data and artificial intelligence (AI) indispensable in drug design. This has necessitated the development of newer algorithms and architectures to ...

Artificial Intelligence Assisted Ultrasonic Extraction of Total Flavonoids from .

Molecules (Basel, Switzerland)
Flavonoids in were studied. The flavonoids in were extracted by ultrasonic method, and the extraction conditions were modeled and optimized by response the surface methodology and the artificial intelligence method. The results show that the ultras...

AI-based monitoring of retinal fluid in disease activity and under therapy.

Progress in retinal and eye research
Retinal fluid as the major biomarker in exudative macular disease is accurately visualized by high-resolution three-dimensional optical coherence tomography (OCT), which is used world-wide as a diagnostic gold standard largely replacing clinical exam...

Automated Data Quality Control in FDOPA brain PET Imaging using Deep Learning.

Computer methods and programs in biomedicine
INTRODUCTION: With biomedical imaging research increasingly using large datasets, it becomes critical to find operator-free methods to quality control the data collected and the associated analysis. Attempts to use artificial intelligence (AI) to per...

Regulating AI in Health Care: The Challenges of Informed User Engagement.

The Hastings Center report
The European Union's proposed Artificial Intelligence Act is a welcome, ambitious law on the regulation of AI systems. However, it underestimates the responsibilities placed on individual users to navigate the implementation of AI. Focusing on the he...

Technical Note: Dose prediction for head and neck radiotherapy using a three-dimensional dense dilated U-net architecture.

Medical physics
PURPOSE: Radiation therapy treatment planning is a time-consuming and iterative manual process. Consequently, plan quality varies greatly between and within institutions. Artificial intelligence shows great promise in improving plan quality and reduc...

[New intraoperative fluorescence-based and spectroscopic imaging techniques in visceral medicine - precision surgery in the "high tech"-operating room].

Zeitschrift fur Gastroenterologie
INTRODUCTION: Fluorescence angiography (FA) with indocyanine green (ICG) and hyperspectral imaging (HSI) are novel intraoperative visualization techniques in abdominal, vascular and transplant surgery. With the purpose of precision surgery, and in or...

REDECA: A Novel Framework to Review Artificial Intelligence and Its Applications in Occupational Safety and Health.

International journal of environmental research and public health
INTRODUCTION: The field of artificial intelligence (AI) is rapidly expanding, with many applications seen routinely in health care, industry, and education, and increasingly in workplaces. Although there is growing evidence of applications of AI in w...

scGCN is a graph convolutional networks algorithm for knowledge transfer in single cell omics.

Nature communications
Single-cell omics is the fastest-growing type of genomics data in the literature and public genomics repositories. Leveraging the growing repository of labeled datasets and transferring labels from existing datasets to newly generated datasets will e...

A Typology of Existing Machine Learning-Based Predictive Analytic Tools Focused on Reducing Costs and Improving Quality in Health Care: Systematic Search and Content Analysis.

Journal of medical Internet research
BACKGROUND: Considerable effort has been devoted to the development of artificial intelligence, including machine learning-based predictive analytics (MLPA) for use in health care settings. The growth of MLPA could be fueled by payment reforms that h...