AIMC Topic: Artificial Intelligence

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Revenue and Cost Analysis of a System Utilizing Natural Language Processing and a Nurse Coordinator for Radiology Follow-up Recommendations.

Current problems in diagnostic radiology
Radiology reports often contain recommendations for follow-up imaging, Provider adherence to these radiology recommendations can be incomplete, which may result in patient harm, lost revenue, or litigation. This study sought to perform a revenue asse...

Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review.

European radiology
OBJECTIVES: Machine learning (ML) for medical imaging is emerging for several organs and image modalities. Our objectives were to provide clinicians with an overview of this field by answering the following questions: (1) How is ML applied in liver c...

Deep learning on independent spatial EEG activity patterns delineates time windows relevant for response inhibition.

Psychophysiology
Inhibitory control processes are an important aspect of executive functions and goal-directed behavior. However, the mostly correlative nature of neurophysiological studies was not able to provide insights which aspects of neural dynamics can best pr...

Artificial intelligence to support person-centred care in breast imaging - A scoping review.

Journal of medical imaging and radiation sciences
AIM: To overview Artificial Intelligence (AI) developments and applications in breast imaging (BI) focused on providing person-centred care in diagnosis and treatment for breast pathologies.

Screening of normal endoscopic large bowel biopsies with interpretable graph learning: a retrospective study.

Gut
OBJECTIVE: To develop an interpretable artificial intelligence algorithm to rule out normal large bowel endoscopic biopsies, saving pathologist resources and helping with early diagnosis.

Diagnosis of Developmental Dysplasia of the Hip by Ultrasound Imaging Using Deep Learning.

Journal of pediatric orthopedics
BACKGROUND: A timely diagnosis of developmental dysplasia of the hip (DDH) is important for satisfactory clinical outcomes. Ultrasonography is a useful tool for DDH screening; however, it is technically demanding. We hypothesized that deep learning c...

A survey of sum-product networks structural learning.

Neural networks : the official journal of the International Neural Network Society
Sum-product networks (SPNs) in deep probabilistic models have made great progress in computer vision, robotics, neuro-symbolic artificial intelligence, natural language processing, probabilistic programming languages, and other fields. Compared with ...

Advancing chemical carcinogenicity prediction modeling: opportunities and challenges.

Trends in pharmacological sciences
Carcinogenicity assessment of any compound is a laborious and expensive exercise with several associated ethical and practical concerns. While artificial intelligence (AI) offers promising solutions, unfortunately, it is contingent on several challen...

Exploring the Advantages of Quantum Generative Adversarial Networks in Generative Chemistry.

Journal of chemical information and modeling
De novo drug design with desired biological activities is crucial for developing novel therapeutics for patients. The drug development process is time- and resource-consuming, and it has a low probability of success. Recent advances in machine learni...