The rapid integration of artificial intelligence (AI) into healthcare has enhanced diagnostic accuracy; however, patient engagement and satisfaction remain significant challenges that hinder the widespread acceptance and effectiveness of AI-driven cl...
Oral cancer has a high mortality rate primarily due to delayed diagnoses, highlighting the need for early detection of oral lesions. This study presents a novel deep learning framework for multi-class classification-based segmentation, enabling accur...
Medical image encryption is important for maintaining the confidentiality of sensitive medical data and protecting patient privacy. Contemporary healthcare systems store significant patient data in text and graphic form. This research proposes a New ...
Congenital glaucoma, a complex and diverse condition, presents considerable difficulties in its identification and categorization. This research used Next Generation Sequencing (NGS) whole-exome data to create a categorization framework using machine...
Optimising healthcare is linked to broadening access to health literacy in Low- and Middle-Income Countries. The safe and responsible deployment of Large Language Models (LLMs) may provide accurate, reliable, and culturally relevant healthcare inform...
Knee injuries are common in several people, frequently controlling for significant injuries and health care costs. This article explains the role of personalized exercise prescriptions in preventing knee injuries. For this purpose, we used the multic...
Deep convolutional neural networks (CNNs) have seen significant growth in medical image classification applications due to their ability to automate feature extraction, leverage hierarchical learning, and deliver high classification accuracy. However...
Sarcopenia, characterized by progressive loss of muscle mass and function, is a growing public health concern. The ZJU index, a novel metabolic marker, integrates lipid metabolism and glucose regulation parameters. While its association with metaboli...
This study aimed to develop and validate a transformer-based early warning score (TEWS) system for predicting adverse events (AEs) in the emergency department (ED). We conducted a retrospective study analyzing adult ED visits at a tertiary hospital. ...
The estimation of rupture risk in Unruptured Intracranial Aneurysm (UIA) constitutes a major area of clinical interest due to the significant morbidity and mortality rates associated with aneurysm rupture. Classic clinical models based on factors suc...
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