Surveys

Attitudes and perceptions of medical researchers towards the use of artificial intelligence chatbots in the scientific process: an international cross-sectional survey.

Chatbots are artificial intelligence (AI) programs designed to simulate conversations with humans that present opportunities and challenges in scientific research. Despite growing clarity from publishing organisations on the use of AI chatbots, researchers' perceptions remain less understood. In this international cross-sectional survey, we aimed to assess researchers' attitudes, familiarity, perc...

Deep learning-based spike sorting: a survey.

Deep learning is increasingly permeating neuroscience, leading to a rise in signal-processing applications for extracellular recordings. These signals capture the activity of small neuronal populations, necessitating 'spike sorting' to assign action potentials (spikes) to their underlying neurons. With the rise in publications delving into new methodologies and techniques for deep learning-based s...

Comparative analysis of volatility forecasting for healthcare stock indices amid public health crises: a study based on the Bayes-CNN model.

In recent years, public health events have significantly impacted various aspects of human production and daily life, particularly in the domains of d...

A comparative study to elucidate factors explaining willingness to use home-care robots in Japan, Ireland, and Finland.

The implementation of home-care robots is sometimes unsuccessful. This study aimed to explore factors explaining people's willingness to use home-care...

Modification and Validation of the System Causability Scale Using AI-Based Therapeutic Recommendations for Urological Cancer Patients: A Basis for the Development of a Prospective Comparative Study.

The integration of artificial intelligence, particularly Large Language Models (LLMs), has the potential to significantly enhance therapeutic decision...

Assessing the Utilization of Large Language Model Chatbots for Educational Purposes by Medical Teachers: A Nationwide Survey From India.

Background Large language models (LLMs) are increasingly explored in healthcare and education. In medical education, they hold the potential to enhanc...

Sociodemographic bias in clinical machine learning models: a scoping review of algorithmic bias instances and mechanisms.

BACKGROUND AND OBJECTIVES: Clinical machine learning (ML) technologies can sometimes be biased and their use could exacerbate health disparities. The ...

Statistical inference and neural network training based on stochastic difference model for air pollution and associated disease transmission.

A polluted air environment can potentially provoke infections of diverse respiratory diseases. The development of mathematical models can study the me...

Development and validation of a prediction model for ED using machine learning: according to NHANES 2001-2004.

Erectile Dysfunction (ED) is a form of sexual dysfunction in males that imposes significant health and financial burdens globally. Despite its high pr...

Interaction-Based Inductive Bias in Graph Neural Networks: Enhancing Protein-Ligand Binding Affinity Predictions From 3D Structures.

Inductive bias in machine learning (ML) is the set of assumptions describing how a model makes predictions. Different ML-based methods for protein-lig...

Feature Selection and Machine Learning Approaches in Prediction of Current E-Cigarette Use Among U.S. Adults in 2022.

Feature selection is essentially the process of picking informative and relevant features from a larger collection of features. Few studies have focus...

AmbiBias Contrast: Enhancing debiasing networks via disentangled space from ambiguity-bias clusters.

The goal of debiasing in classification tasks is to train models to be less sensitive to correlations between a sample's target attribution and period...

Knowledge-based inductive bias and domain adaptation for cell type annotation.

Measurement techniques often result in domain gaps among batches of cellular data from a specific modality. The effectiveness of cross-batch annotatio...

Misidentifications in ayurvedic medicinal plants: Convolutional neural network (CNN) to overcome identification confusions.

Plants are a vital ingredient of traditional medicine in Sri Lanka, and the quantity of medicinal plants used as a number differs in literature. Field...

The Potential of Artificial Intelligence Tools for Reducing Uncertainty in Medicine and Directions for Medical Education.

In the field of medicine, uncertainty is inherent. Physicians are asked to make decisions on a daily basis without complete certainty, whether it is i...

The bias algorithm: how AI in healthcare exacerbates ethnic and racial disparities - a scoping review.

This scoping review examined racial and ethnic bias in artificial intelligence health algorithms (AIHA), the role of stakeholders in oversight, and th...

A survey on representation learning for multi-view data.

Multi-view clustering has become a rapidly growing field in machine learning and data mining areas by combining useful information from different view...

GradToken: Decoupling tokens with class-aware gradient for visual explanation of Transformer network.

Transformer networks have been widely used in the fields of computer vision, natural language processing, graph-structured data analysis, etc. Subsequ...

Automated speech recognition bias in personnel selection: The case of automatically scored job interviews.

Organizations, researchers, and software increasingly use automatic speech recognition (ASR) to transcribe speech to text. However, ASR can be less ac...

Diagnosis of Pancreatic Ductal Adenocarcinoma Using Deep Learning.

Recent advances in artificial intelligence (AI) research, particularly in image processing technologies, have shown promising applications across vari...