Artificial Intelligence Medical Compendium

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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 

Recently, convolutional neural networks (CNNs) have made significant strides in hyperspectral image classification (HSIC) tasks by contextualizing the convolutional kernels as global as possible. However, as the kernel sizes increase, encoding multi-... read more 

Multivariate time series (MTS) forecasting plays a pivotal role in the digitalization and intelligent development of modern society, while previous MTS forecasting methods based on deep learning often rely on capturing intra-series dependencies for m... read more 

Deploying Deep Learning algorithms in the real world requires some care that is generally not considered in the training procedure. In real-world scenarios, where the input data cannot be controlled, it is important for a model to identify when a sam... read more