Medical Education

Artificial Intelligence in Radiology Residency Training.

Artificial intelligence (AI) is an emerging technology that brings a wide array of new tools to the field of radiology. AI will certainly have an impact on the day-to-day work of radiologists in the coming decades, thus training programs must prepare radiology residents adequately for their future careers. Radiology training programs should aim to give residents an understanding of the fundamental...

Artificial Intelligence in Health Care: Insights From an Educational Forum.

Discussions surrounding the future of artificial intelligenc (AI) in healthcare often cause consternation among healthcare professionals. These feelings may stem from a lack of formal education on AI and how to be a leader of AI implementation in medical systems. To address this, our academic medical center hosted an educational summit exploring how to become a leader of AI in healthcare. This art...

DMCNN: A Deep Multiscale Convolutional Neural Network Model for Medical Image Segmentation.

Medical image segmentation is one of the hot issues in the related area of image processing. Precise segmentation for medical images is a vital guaran...

Evaluation of the effect of Lactobacillus sakei strain L115 on Listeria monocytogenes at different conditions of temperature by using predictive interaction models.

In this study, the inhibitory capacity of Lactobacillus sakei strain L115 against Listeria monocytogenes has been assayed at 4, 8, 11, 15 and 20 °C in...

A novel machine learning based computational framework for homogenization of heterogeneous soft materials: application to liver tissue.

Real-time simulation of organs increases comfort and safety for patients during the surgery. Proper generalized decomposition (PGD) is an efficient nu...

Emergency Department Capacity Planning: A Recurrent Neural Network and Simulation Approach.

Emergency departments (EDs) play a vital role in the whole healthcare system as they are the first point of care in hospitals for urgent and criticall...

Humans and machines: Moving towards a more symbiotic approach to learning clinical reasoning.

Artificial intelligence is a growing phenomenon that is driving major changes to how we deliver healthcare. One of its most significant and challengin...

An in vitro assay and artificial intelligence approach to determine rate constants of nanomaterial-cell interactions.

In vitro assays and simulation technologies are powerful methodologies that can inform scientists of nanomaterial (NM) distribution and fate in humans...

Artificial neural network metamodel for sensitivity analysis in a total hip replacement health economic model.

: Metamodels have been used to approximate complex simulations and have many applications with sensitivity analysis, optimization, etc. However, their...

NNVA: Neural Network Assisted Visual Analysis of Yeast Cell Polarization Simulation.

Complex computational models are often designed to simulate real-world physical phenomena in many scientific disciplines. However, these simulation mo...

Force classification during robotic interventions through simulation-trained neural networks.

PURPOSE: Intravitreal injection is among the most frequent treatment strategies for chronic ophthalmic diseases. The last decade has seen a serious in...

Learning the implicit strain reconstruction in ultrasound elastography using privileged information.

Quasi-static ultrasound elastography is an importance imaging technology to assess the conditions of various diseases through reconstructing the tissu...

Analysis of crystallization phenomenon in Indian honey using molecular dynamics simulations and artificial neural network.

Molecular dynamics simulation was performed on sugar profile and moisture content-based mixture systems of six Indian honey samples. Comparative studi...

Artificial Intelligence in Medical Education: Best Practices Using Machine Learning to Assess Surgical Expertise in Virtual Reality Simulation.

OBJECTIVE: Virtual reality simulators track all movements and forces of simulated instruments, generating enormous datasets which can be further analy...

Self-Paced Balance Learning for Clinical Skin Disease Recognition.

Class imbalance is a challenging problem in many classification tasks. It induces biased classification results for minority classes that contain less...

MCRDR Knowledge-Based 3D Dialogue Simulation in Clinical Training and Assessment.

Dialogue-based simulation is a real-world practice technique for medical and clinical education that provides students with an opportunity to train us...

Predicting Reaction Products and Automating Reactive Trajectory Characterization in Molecular Simulations with Support Vector Machines.

A machine learning-based methodology for the prediction of chemical reaction products, along with automated elucidation of mechanistic details via pha...

Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning.

Good quality of medical images is a prerequisite for the success of subsequent image analysis pipelines. Quality assessment of medical images is there...

Quantitative Prediction of the Landscape of T Cell Epitope Immunogenicity in Sequence Space.

Immunodominant T cell epitopes preferentially targeted in multiple individuals are the critical element of successful vaccines and targeted immunother...

Deep Learning and Random Forest Approach for Finding the Optimal Traditional Chinese Medicine Formula for Treatment of Alzheimer's Disease.

It has demonstrated that glycogen synthase kinase 3β (GSK3β) is related to Alzheimer's disease (AD). On the basis of the world largest traditional Chi...