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The EU medical device regulation: Implications for artificial intelligence-based medical device software in medical physics.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Medical device manufacturers are increasingly applying artificial intelligence (AI) to innovate their products and to improve patient outcomes. Health institutions are also developing their own algorithms, to address specific needs for which no comme...

Artificial intelligence in the medical physics community: An international survey.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: To assess current perceptions, practices and education needs pertaining to artificial intelligence (AI) in the medical physics field.

A Dual-Dimer method for training physics-constrained neural networks with minimax architecture.

Neural networks : the official journal of the International Neural Network Society
Data sparsity is a common issue to train machine learning tools such as neural networks for engineering and scientific applications, where experiments and simulations are expensive. Recently physics-constrained neural networks (PCNNs) were developed ...

Gradient-based training and pruning of radial basis function networks with an application in materials physics.

Neural networks : the official journal of the International Neural Network Society
Many applications, especially in physics and other sciences, call for easily interpretable and robust machine learning techniques. We propose a fully gradient-based technique for training radial basis function networks with an efficient and scalable ...

Targeted transfer learning to improve performance in small medical physics datasets.

Medical physics
PURPOSE: To perform an in-depth evaluation of current state of the art techniques in training neural networks to identify appropriate approaches in small datasets.

Self-supervised learning of physics-guided reconstruction neural networks without fully sampled reference data.

Magnetic resonance in medicine
PURPOSE: To develop a strategy for training a physics-guided MRI reconstruction neural network without a database of fully sampled data sets.

An introduction to deep learning in medical physics: advantages, potential, and challenges.

Physics in medicine and biology
As one of the most popular approaches in artificial intelligence, deep learning (DL) has attracted a lot of attention in the medical physics field over the past few years. The goals of this topical review article are twofold. First, we will provide a...

From inert matter to the global society life as multi-level networks of processes.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
A few billion years have passed since the first life forms appeared. Since then, life has continued to forge complex associations between the different emergent levels of interconnection it forms. The advances of recent decades in molecular chemistry...

Deep multiphysics: Coupling discrete multiphysics with machine learning to attain self-learning in-silico models replicating human physiology.

Artificial intelligence in medicine
OBJECTIVES: The objective of this study is to devise a modelling strategy for attaining in-silico models replicating human physiology and, in particular, the activity of the autonomic nervous system.

Physically informed artificial neural networks for atomistic modeling of materials.

Nature communications
Large-scale atomistic computer simulations of materials heavily rely on interatomic potentials predicting the energy and Newtonian forces on atoms. Traditional interatomic potentials are based on physical intuition but contain few adjustable paramete...