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A comparative study of pre-trained language models for named entity recognition in clinical trial eligibility criteria from multiple corpora
BackgroundClinical trial protocols are the foundation for advancing medical sciences, however, the extraction of accurate and meaningful information...
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Enhancing electromagnetic tracking accuracy in medical applications using pre-trained witness sensor distortion models
PurposeElectromagnetic tracking (EMT) accuracy is affected by the presence of surrounding metallic materials. In this work, we propose measuring the...
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Drug knowledge discovery via multi-task learning and pre-trained models
BackgroundDrug repurposing is to find new indications of approved drugs, which is essential for investigating new uses for approved or...
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CheSS: Chest X-Ray Pre-trained Model via Self-supervised Contrastive Learning
Training deep learning models on medical images heavily depends on experts’ expensive and laborious manual labels. In addition, these images, labels,...
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Monkeypox Virus Detection Using Pre-trained Deep Learning-based Approaches
Monkeypox virus is emerging slowly with the decline of COVID-19 virus infections around the world. People are afraid of it, thinking that it would...
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Evaluating the performance of Generative Pre-trained Transformer-4 (GPT-4) in standardizing radiology reports
ObjectiveRadiology reporting is an essential component of clinical diagnosis and decision-making. With the advent of advanced artificial intelligence...
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Preliminary assessment of automated radiology report generation with generative pre-trained transformers: comparing results to radiologist-generated reports
PurposeIn this preliminary study, we aimed to evaluate the potential of the generative pre-trained transformer (GPT) series for generating radiology...
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Blepharoptosis Consultation with Artificial Intelligence: Aesthetic Surgery Advice and Counseling from Chat Generative Pre-Trained Transformer (ChatGPT)
BackgroundChat generative pre-trained transformer (ChatGPT) is a publicly available extensive artificial intelligence (AI) language model that...
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Human Monkeypox Classification from Skin Lesion Images with Deep Pre-trained Network using Mobile Application
Recently, human monkeypox outbreaks have been reported in many countries. According to the reports and studies, quick determination and isolation of...
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Event-Based Clinical Finding Extraction from Radiology Reports with Pre-trained Language Model
Radiology reports contain a diverse and rich set of clinical abnormalities documented by radiologists during their interpretation of the images....
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Fusing pre-trained convolutional neural networks features for multi-differentiated subtypes of liver cancer on histopathological images
Liver cancer is a malignant tumor with high morbidity and mortality, which has a tremendous negative impact on human survival. However, it is a...
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Development and evaluation of a program based on a generative pre-trained transformer model from a public natural language processing platform for efficiency enhancement in post-procedural quality control of esophageal endoscopic submucosal dissection
BackgroundPost-procedural quality control of endoscopic submucosal dissection (ESD) is emphasized in guidelines. However, this process can be tedious...
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End-to-end pseudonymization of fine-tuned clinical BERT models
Many state-of-the-art results in natural language processing (NLP) rely on large pre-trained language models (PLMs). These models consist of large...
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The Impact of Pre-Exercise Carbohydrate Meal on the Effects of Yerba Mate Drink on Metabolism, Performance, and Antioxidant Status in Trained Male Cyclists
IntroductionThe consumption of yerba mate (YM), a source of antioxidants, in a fasted state increases fatty acid oxidation (FAT ox ) during...
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Exploring the performance and explainability of fine-tuned BERT models for neuroradiology protocol assignment
BackgroundDeep learning has demonstrated significant advancements across various domains. However, its implementation in specialized areas, such as...
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From CNN to Transformer: A Review of Medical Image Segmentation Models
Medical image segmentation is an important step in medical image analysis, especially as a crucial prerequisite for efficient disease diagnosis and...
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Extracting patient lifestyle characteristics from Dutch clinical text with BERT models
BackgroundBERT models have seen widespread use on unstructured text within the clinical domain. However, little to no research has been conducted...
