Background/Objectives: Solid organ transplantation remains a critical life-saving treatment for end-stage organ failure, yet it faces persistent challenges, such as organ scarcity, graft rejection, and postoperative complications. Artificial intelligence (AI) has the potential to address these challenges by revolutionizing transplantation practices. Methods: This review article explores the diverse applications of AI in solid organ transplantation, focusing on its impact on diagnostics, treatment, and the evolving market landscape. We discuss how machine learning, deep learning, and generative AI are harnessing vast datasets to predict transplant outcomes, personalized immunosuppressive regimens, and optimize patient selection. Additionally, we examine the ethical implications of AI in transplantation and highlight promising AI-driven innovations nearing FDA evaluation. Results: AI improves organ allocation processes, refines predictions for transplant outcomes, and enables tailored immunosuppressive regimens. These advancements contribute to better patient selection and enhance overall transplant success rates. Conclusions: By bridging the gap in organ availability and improving long-term transplant success, AI holds promise to significantly advance the field of solid organ transplantation.

Sign languages are characterized by complex and subtle hand movements, which are challenging for computer vision systems to accurately recognize. This study suggests an innovative deep learning pipeline specifically designed for reliable gesture recognition of Kazakh Sign Language. This approach combines key point detection using the YOLO model, optical flow estimation, and a bidirectional long short-term memory (BiLSTM) network. At the initial stage, a dataset is generated using MediaPipe, which is then used to train the YOLO model in order to accurately identify key hand points. After training, the YOLO model extracts key points and bounding boxes from video recordings of gestures, creating consistent representations of movements. To improve the recognition of dynamic gestures, the optical flow is calculated in an area covering 10% of the area around key points, which allows the dynamics of movements to be captured and provides additional time characteristics. The BiLSTM network is trained on multimodal input that combines data on keypoints, bounding boxes, and optical flow, resulting in improved gesture classification accuracy. The experimental results demonstrate that the proposed approach is superior to traditional methods based solely on key points, especially in recognizing fast and complex gestures. The proposed structure promotes the development of sign language recognition technologies, especially for poorly studied languages such as Kazakh, paving the way to more inclusive and effective communication tools.

The optimization capabilities of Kolmogorov–Arnold Networks (KANs) remain largely unexplored, which has limited their practical use in video anomaly recognition compared to conventional 3D-CNNs. To address this gap, we introduce a novel hybrid optimization framework that combines a Minimax Ant System (MMAS) for hyperparameter selection with a modified DARTS strategy for adaptive tuning of the 3D KAN architecture. Unlike existing approaches, our method simultaneously optimizes both learning dynamics and architectural configurations, enabling KANs to better exploit their expressive power in spatiotemporal feature extraction. Applied to a three-class video dataset, the proposed approach improved model accuracy to 87%, surpassing the performance of a standard 3D-CNN by 6%.

The field of medicine is witnessing rapid development of AI, highlighting the importance of proper data processing. However, when working with medical data, there is a problem of class imbalance, where the amount of data about healthy patients significantly exceeds the amount of data about sick ones. This leads to incorrect classification of the minority class, resulting in inefficient operation of machine learning algorithms. In this study, a hybrid method was developed to address the problem of class imbalance, combining oversampling (GenSMOTE) and undersampling (ENN) algorithms. GenSMOTE used frequency oversampling optimization based on a genetic algorithm, selecting the optimal value using a fitness function. The next stage implemented an ensemble method based on stacking, consisting of three base (k-NN, SVM, LR) and one meta-model (Decision Tree). The hyperparameters of the meta-model were optimized using the GridSearchCV algorithm. During the study, datasets on diabetes, liver diseases, and brain glioma were used. The developed hybrid class balancing method significantly improved the quality of the model: the F1-score increased by 10-75%, and accuracy by 5-30%. Each stage of the hybrid algorithm was visualized using a nonlinear UMAP algorithm. The ensemble method based on stacking, in combination with the hybrid class balancing method, demonstrated high efficiency in solving classification tasks in medicine. This approach can be applied for diagnosing various diseases, which will increase the accuracy and reliability of forecasts. It is planned to expand the application of this approach to large volumes of data and improve the oversampling algorithm using additional capabilities of the genetic algorithm.

Methylomics has emerged as a central framework for understanding gene regulation in development and disease, yet the rapid expansion of profiling technologies, computational integration methods, and clinical applications has outpaced comprehensive synthesis. This review addresses that gap by systematically examining current advances across the full methylomics pipeline, from data generation to clinical translation. We draw on evidence from large-scale consortium datasets and benchmarking studies of multi-omics integration methods including MOFA, DIABLO, and deep learning architectures, single-cell and spatial methylomic technologies, long-read sequencing platforms (Oxford Nanopore, PacBio HiFi), and cell-free DNA (cfDNA) liquid biopsy approaches. The review further surveys methylation dysregulation across major disease domains, including cancer, cardiovascular disease, neurological disorders, and autoimmune conditions. Integrating methylomic data with transcriptomic and chromatin accessibility layers, particularly in spatial and single-cell contexts, substantially improves the resolution of disease-associated regulatory mechanisms. cfDNA methylation profiling emerges as a cross-disease, non-invasive monitoring platform with broad diagnostic potential, supported by machine learning-based deconvolution. We conclude that while technological barriers are diminishing, standardization of analytical workflows, population diversity in reference datasets, and regulatory alignment remain the principal challenges for translating methylomics advances into broadly accessible precision medicine.

The issue of class imbalance in medical data poses a significant challenge for developing robust machine learning models aimed at medical diagnosis. A characteristic feature of such data is the substantial dominance of instances belonging to majority classes (e.g., healthy patients or those with common diseases) over instances representing rare conditions. This disproportion leads to machine learning models trained on such data being prone to systematic classification errors, predominantly predicting the most frequent class. Consequently, the ability of models to accurately identify rare cases is severely diminished. This paper proposes a hybrid class-balancing algorithm that combines Inverse Quadratic Radial Undersampling (IQRBU) and genetic oversampling to address this issue. The integration of these two methods within a single algorithm achieves an optimal balance between preserving information and enhancing the representation of rare classes. Experimental results conducted on several medical datasets demonstrated the effectiveness of the proposed approach. The obtained results showed that the hybrid algorithm significantly improves classification metrics, such as the F1-score and accuracy. These findings underscore the potential of our approach to enhance the reliability and precision of medical diagnostic systems.