Artificial Intelligence Medical Compendium

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 

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OBJECTIVES: This study aimed to develop and validate a two-stage deep learning method for diagnosing oral potentially malignant disorders (OPMDs). We also compared its diagnostic performance with that of clinicians at different levels of seniority an... read more 

Peanuts are susceptible to mildew which are harmful to human health. In this research, Raman hyperspectral imaging technique was used to analyze moldy peanuts of mixed varieties. One-dimensional spectral data and two-dimensional spatial information w... read more 

Robust optimization problems subject to norm uncertainty appear in numerous applications in various fields such as engineering, logistics, and finance. Despite its importance, robust optimization algorithms face significant computational challenges f... read more 

OBJECTIVE: This study aimed to compare extraction versus orthodontic eruption decisions for impacted maxillary canines made by three artificial intelligence-based chatbots (ChatGPT, Gemini, and Grok) with those made by orthodontist raters, and to eva... read more 

Bloodstain pattern analysis (BPA) is increasingly shifting towards more objective methodologies for pattern classification. This transition can involve image-processing techniques that extract observable bloodstain features as data for pattern classi... read more 

Graphs play a critical role in capturing complex data relationships, particularly in few-shot learning tasks. However, one of the major challenges in graph-based models, such as Graph Neural Networks (GNNs), is the issue of over-smoothing, which dimi... read more