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Innovations in surgical training: exploring the role of artificial intelligence and large language models (LLM).

Revista do Colegio Brasileiro de Cirurgioes
The landscape of surgical training is rapidly evolving with the advent of artificial intelligence (AI) and its integration into education and simulation. This manuscript aims to explore the potential applications and benefits of AI-assisted surgical ...

Comparing feedforward neural networks using independent component analysis on hidden units.

PloS one
Neural networks are widely used for classification and regression tasks, but they do not always perform well, nor explicitly inform us of the rationale for their predictions. In this study we propose a novel method of comparing a pair of different fe...

Artificial Intelligence Improves Novices' Bronchoscopy Performance: A Randomized Controlled Trial in a Simulated Setting.

Chest
BACKGROUND: Navigating through the bronchial tree and visualizing all bronchial segments is the initial step toward learning flexible bronchoscopy. A novel bronchial segment identification system based on artificial intelligence (AI) has been develop...

A multilayered bidirectional associative memory model for learning nonlinear tasks.

Neural networks : the official journal of the International Neural Network Society
A multilayered bidirectional associative memory neural network is proposed to account for learning nonlinear types of association. The model (denoted as the MF-BAM) is composed of two modules, the Multi-Feature extracting bidirectional associative me...

Drop edges and adapt: A fairness enforcing fine-tuning for graph neural networks.

Neural networks : the official journal of the International Neural Network Society
The rise of graph representation learning as the primary solution for many different network science tasks led to a surge of interest in the fairness of this family of methods. Link prediction, in particular, has a substantial social impact. However,...

Evaluating the Use of Graph Neural Networks and Transfer Learning for Oral Bioavailability Prediction.

Journal of chemical information and modeling
Oral bioavailability is a pharmacokinetic property that plays an important role in drug discovery. Recently developed computational models involve the use of molecular descriptors, fingerprints, and conventional machine-learning models. However, dete...

Spatial oblivion channel attention targeting intra-class diversity feature learning.

Neural networks : the official journal of the International Neural Network Society
Convolutional neural networks (CNNs) have successfully driven many visual recognition tasks including image classification. However, when dealing with classification tasks with intra-class sample style diversity, the network tends to be disturbed by ...

Long- and short-term history effects in a spiking network model of statistical learning.

Scientific reports
The statistical structure of the environment is often important when making decisions. There are multiple theories of how the brain represents statistical structure. One such theory states that neural activity spontaneously samples from probability d...

ChatGPT-A double-edged sword for healthcare education? Implications for assessments of dental students.

European journal of dental education : official journal of the Association for Dental Education in Europe
INTRODUCTION: Open-source generative artificial intelligence (AI) applications are fast-transforming access to information and allow students to prepare assignments and offer quite accurate responses to a wide range of exam questions which are routin...

Self-Attentive Channel-Connectivity Capsule Network for EEG-Based Driving Fatigue Detection.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Deep neural networks have recently been successfully extended to EEG-based driving fatigue detection. Nevertheless, most existing models fail to reveal the intrinsic inter-channel relations that are known to be beneficial for EEG-based classification...