Dental caries is the most frequent dental health issue in the general population. Dental caries can result in extreme pain or infections, lowering people’s quality of life. Applying machine learning models to automatically identify dental caries can lead to earlier treatment. However, physicians frequently find the model results unsatisfactory due to a lack of explainability. Our study attempts to address this issue with an explainable deep learning model for detecting dental caries. We tested three prominent pre-trained models, EfficientNet-B0, DenseNet-121, and ResNet-50, to determine which is best for the caries detection task. These models take panoramic images as the input, producing a caries–non-caries classification result and a heat map, which visualizes areas of interest on the tooth. The model performance was evaluated using whole panoramic images of 562 subjects. All three models produced remarkably similar results. However, the ResNet-50 model exhibited a slightly better performance when compared to EfficientNet-B0 and DenseNet-121. This model obtained an accuracy of 92.00%, a sensitivity of 87.33%, and an F1-score of 91.61%. Visual inspection showed us that the heat maps were also located in the areas with caries. The proposed explainable deep learning model diagnosed dental caries with high accuracy and reliability. The heat maps help to explain the classification results by indicating a region of suspected caries on the teeth. Dentists could use these heat maps to validate the classification results and reduce misclassification.

The amount of data produced significantly increased with the development of Internet technologies. Accordingly, the importance of natural language processing studies increased, and this topic became one of the most studied artificial intelligence subjects. Even though it is a popular topic that is widely studied on, not enough studies have been conducted on the Turkish language. Even the studies conducted in Turkey are primarily on English and other natural languages instead of Turkish. The lack of a Turkish dataset is the most crucial reason for the lack of studies. Therefore, to create a solution, user reviews on e-commerce websites were collected and labelled reviews as positive, negative and neutral, and a new and unique dataset consisting of 150,000 reviews was created. This dataset was named TRSAv1, which was publicly shared with the researchers will contribute to the Turkish natural language processing studies; however, the effect of different word representation methods on algorithm performance was examined in detail, and the results were compared.

Objectives: Breast cancer is a type of cancer caused by the uncontrolled growth of cells in the breast tissue. In a few cases, erroneous diagnosis of breast cancer by specialists and unnecessary biopsies can lead to various negative consequences. In some cases, radiologic examinations or clinical findings may raise the suspicion of breast cancer, but subsequent detailed evaluations may not confirm cancer. In addition to causing unnecessary anxiety and stress to patients, such diagnosis can also lead to unnecessary biopsy procedures, which are painful, expensive, and prone to misdiagnosis. Therefore, there is a need for the development of more accurate and reliable methods for breast cancer diagnosis.Methods: In this study, we proposed an artificial intelligence (AI)‐based method for automatically classifying breast solid mass lesions as benign vs malignant. In this study, a new breast cancer dataset (Breast‐XD) was created with 791 solid mass lesions belonging to 752 different patients aged 18 to 85 years, which were examined by experienced radiologists between 2017 and 2022.Results: Six classifiers, support vector machine (SVM), K‐nearest neighbor (K‐NN), random forest (RF), decision tree (DT), logistic regression (LR), and XGBoost, were trained on the training samples of the Breast‐XD dataset. Then, each classifier made predictions on 159 test data that it had not seen before. The highest classification result was obtained using the explainable XGBoost model (X²GAI) with an accuracy of 94.34%. An explainable structure is also implemented to build the reliability of the developed model.Conclusions: The results obtained by radiologists and the X²GAI model were compared according to the diagnosis obtained from the biopsy. It was observed that our developed model performed well in cases where experienced radiologists gave false positive results.

Transformers tabanlı ile duygu sınıflandırma, son zamanlarda doğal dil işleme ve makine öğrenmesi alanında yaygın olarak çalışılan bir konudur. Metinler içerisinde karşılaşılan duygusal ifadelerin anlamlandırılması ve sınıflandırılması, sosyal medya analizi, piyasa araştırması, kullanıcı deneyimleri vb. gibi kullanılabileceği birçok alan mevcuttur. Bu sebeple, bu çalışmada Transformers tabanlı mimariler kullanılarak duygu sınıflandırmasının gerçekleştirilmesi hedeflenmiştir. Bu çalışmada, 150000 veriden oluşan TRSAv1 veriseti üzerinde, 8 farklı BERTurk ve 2 farklı ELECTRA varyasyonu üzerinde duygu sınıflandırma işlemi için kullanılmıştır. Bu modeller, Türkçe metinler üzerinde duygu sınıflandırılması çalışmalarında kullanılmak için önceden eğitilmiş modellerdir. Veri seti üzerinde 3 farklı metot kullanılarak modeller eğitilmiş ve sonuçlar karşılaştırmalı olarak değerlendirilmiştir. Yapılan deneyler sonucunda, modellerin duygu sınıflandırma performansları doğruluk ve F1-skor metrikleri kullanılarak ölçülmüştür. Deney sonuçları, Transformers modellerinin duygu sınıflandırması konusundaki etkinliğini ve kullanılan modellerin performans değerlendirmelerini ortaya koymuştur.

Lumbar spinal stenosis (LSS) involves the narrowing of the spinal canal, leading to compression of the spinal cord and nerves in the lower back. Common causes include injuries, degenerative age-related changes, congenital conditions, and tumors, all of which contribute to back pain. Early diagnosis is critical for symptom management, preventing progression, and preserving quality of life. This study systematically reviews AI-based approaches for predicting LSS using MRI axial and sagittal imaging. The review focuses on various AI tasks: detection, segmentation, classification, hybrid approaches, spinal index measurements (SIM), and explainable AI frameworks. The aim is to highlight current knowledge, identify limitations in existing models, and propose future research directions. Following PRISMA guidelines and the PICO method (Population, Intervention, Comparison, Outcome), the review collects data from databases like PubMed, Web of Science, ScienceDirect, and IEEE Xplore (2005–2024). The Rayyan AI tool is used for duplicate removal and screening. The screening process includes an initial review of titles and abstracts, followed by full-text appraisal. The Meta Quality Appraisal Tool (MetaQAT) assesses the quality of selected articles. Of 1323 records, 97 duplicates were removed. After screening, 895 records were excluded, leaving 331 for full-text review. Among these, 184 articles were excluded for lacking AI relevance. Ultimately, 95 key articles (91 technical papers and 4 reviews) were identified for their contributions to AI-based LSS prediction. This review provides a comprehensive analysis of AI techniques in LSS prediction, guiding future research and advancing understanding in areas like explainable AI and large language models (LLMs).