Artificial Intelligence Medical Compendium

OBJECTIVES: This study explores the perspectives of dental students in Saudi Arabia regarding the integration of large language models (LLMs) in dental education. It aims to understand their familiarity, utilisation and perceptions of these tools, wh... read more 

OBJECTIVE: Accurate assessment of foot and ankle alignment through clinical measurements is essential for diagnosing deformities, treatment planning, and monitoring outcomes. The traditional 2D radiographs fail to fully represent the 3D complexity of... read more 

The recycling of spent lead-acid batteries has become increasingly crucial for lead (Pb) supply, yet it generates significant secondary pollutants including lead dust, water-quenched slag (WQS) and wastewater that threaten soil and groundwater qualit... read more 

Chronic social isolation stress (CSIS) is a well-established preclinical model for studying stress-induced neurobiological changes and their effects on behavior and brain function in depression. The prefrontal cortex (PFC), a brain region essential f... read more 

OBJECTIVE: To develop and validate an artificial intelligence-based tool for the diagnosis of osteoporosis/osteopenia using hip radiographs. The tool aims to classify femurs into risk-based categories for osteoporosis/osteopenia, enabling patient pri... read more 

Recent advances in deep learning have significantly improved skin cancer classification, yet concerns regarding algorithmic fairness persist because of performance disparities across skin tone groups. Existing methods often attempt to mitigate bias b... read more 

Gastric cancer is among the most common diseases worldwide and can lead to fatal outcomes. Early diagnosis significantly increases the success of treatment, and accurate and rapid analysis of histopathological images is of enormous importance. Howeve... read more 

Accurate classification of anatomical regions in computed tomography (CT) scans is essential for optimizing downstream diagnostic and analytic workflows in medical imaging. We demonstrate the high performance that deep learning (DL) algorithms can ac... read more 

Denoising Diffusion Probabilistic Models (DDPM) have emerged as a promising generative framework for sample synthesis, yet their limitations in detail preservation hinder practical applications in computed tomography (CT) image reconstruction. To add... read more 

AIM: To determine the perceptions and readiness of nurses regarding the integration of artificial intelligence (AI) into healthcare services. DESIGN: A descriptive cross-sectional study. METHODS: Data were collected from 388 nurses across Turkey usin... read more