INTRODUCTION: The integration of artificial intelligence in mental health care represents a transformative shift in the identification, treatment, and management of mental disorders. This systematic review explores the diverse applications of artific...
BACKGROUND: Sepsis remains a life-threatening condition in intensive care units (ICU) with high morbidity and mortality rates. Some biomarkers commonly used in clinic do not have the characteristics of rapid and specific growth and rapid decline afte...
Speech emotion recognition has seen a surge in transformer models, which excel at understanding the overall message by analyzing long-term patterns in speech. However, these models come at a computational cost. In contrast, convolutional neural netwo...
Big data can revolutionize research and quality improvement for cardiac ultrasound. Text reports are a critical part of such analyses. Cardiac ultrasound reports include structured and free text and vary across institutions, hampering attempts to min...
In the medical field, text annotation involves categorizing clinical and biomedical texts with specific medical categories, enhancing the organization and interpretation of large volumes of unstructured data. This process is crucial for developing to...
While multi-modal deep learning approaches trained using magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG PET) data have shown promise in the accurate identification of Alzheimer's disease, their clinical appl...
Azoospermia, defined by the absence of sperm in the ejaculate, manifests as obstructive azoospermia (OA) or non-obstructive azoospermia (NOA). Reliable predictive models utilizing biomarkers could aid in clinical decision-making. This study included ...
This paper explores the potential of transformer-based foundation models to detect Atrial Fibrillation (AFIB) in electrocardiogram (ECG) processing, an arrhythmia specified as an irregular heart rhythm without patterns. We construct a language with t...
Over the past few years, surgical data science has attracted substantial interest from the machine learning (ML) community. Various studies have demonstrated the efficacy of emerging ML techniques in analysing surgical data, particularly recordings o...
Diagnosing and characterizing biliary strictures (BS) remains challenging. Artificial intelligence (AI) applied to digital single-operator cholangioscopy (D-SOC) holds promise for improving diagnostic accuracy in indeterminate BS. This multicenter st...
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