# Application of Artificial Intelligence in the Practice of Medicine
## 2 Review of Issue Contents  
This Special Issue presents ten original papers that cover the latest technologies and advances in the design of intelligent medical systems and applications. Moreover, each paper contributes to research that affords insights into the processing of medical data collected from patients. Visual acuity (VA) measures the ability to distinguish the shapes and details of objects at a given distance. However, in some cases, such as unconsciousness or disease e.g., dementia, it may be impossible to measure VA using traditional chart-based methods. In [3], [PERSON] et al. propose a machine-learning-based VA measurement method that determines VA from fundus images only. Three models, SVM, VGG-19, and EfficientNet-B7, were ensembled to predict categories. This is a precedent for applying artificial intelligence in medical practice to measure VA using fundus images. Neuroimaging must often process a large amount of data with significantly fewer cases than the number of variables, which results in overmatching. To prevent this problem, [PERSON] et al. [4] propose a new dual Bayesian linear regression model with feature selection (DBL-FS) that effectively reduces the number of samples with high-dimensional features. This relies on including an automatic relevance determination prior (ARD) over the weight matrices, which automatically infers the features' relevance in the input feature space by assigning higher/lower relevance values when they contain more/fewer relevant features. In addition, the DBL-FS Bayesian approach facilitated prior expert knowledge to guide the FS process and compensated for the limited number of samples available to train the model. The advantage of using DBL-FS allowed the detection and characterization of morphometric brain changes in a schizophrenic rodent model. Image segmentation is used to analyze medical images quantitatively for diagnosis and treatment planning. This is because manual segmentation requires considerable expert effort and time. [PERSON] et al. [5] propose a deep learning tool that easily creates training data to mitigate this inconvenience. This study was performed using two types of information: visual features and organ segment locations. The proposed model consists of two submodules: a feature encoder and a kernel function. The kernel function incorporates feature similarity density and Gaussian kernel density. The tool demonstrates competitive results when compared to state-of-the-art segmentation algorithms, such as UNet and DeepNetV3. The tool can be trained with minimal labeled data, uses anchor pixels from user interactions to segment organs easily, and refines the segmentation results by modifying the thresholds. [PERSON] et al. [6] used machine learning to predict whether patients with schizophrenia exhibit aggressive behaviors. Up-sampling was used to process a small number of categories to balance the data, reduce variables using the random forest algorithm, and build machine learning models by including the logistic regression, trees, random forest, gradient boosting, k-nearest neighbor (KNN), support vector machines (SVM), and naive Bayes approaches. The performance of the SVM model was superior to the other machine learning algorithms. Negative behavior towards other patients was identified as the most indicative factor for distinguishing aggressive from non-aggressive patients. Its application may enable clinicians to identify high-risk patients at an early stage, modify their treatment accordingly, and prevent aggressive events during hospitalization. Identifying the locations and extent of brain infarctions is essential for diagnosis and treatment. In general, deep learning requires large amounts of training data. To overcome this problem, [PERSON] et al. [7] generated pseudo-patient images using CycleGAN, which performed image transformation without paired images. First, CycleGAN was used for data augmentation and to generate pseudo-cerebal infarction images from images of healthy specimens.