The rapid evolution of artificial intelligence (AI), particularly in deep learning, has significantly impacted radiology, introducing an array of AI solutions for interpretative tasks. This paper provides radiology departments with a practical guide for selecting and integrating AI solutions, focusing on interpretative tasks that require the active involvement of radiologists. Our approach is not to list available applications or review scientific evidence, as this information is readily available in previous studies; instead, we concentrate on the essential factors radiology departments must consider when choosing AI solutions. These factors include clinical relevance, performance and validation, implementation and integration, clinical usability, costs and return on investment, and regulations, security, and privacy. We illustrate each factor with hypothetical scenarios to provide a clearer understanding and practical relevance. Through our experience and literature review, we provide insights and a practical roadmap for radiologists to navigate the complex landscape of AI in radiology. We aim to assist in making informed decisions that enhance diagnostic precision, improve patient outcomes, and streamline workflows, thus contributing to the advancement of radiological practices and patient care.

Background/Objectives: Monkeypox is a zoonotic virus that presents with smallpox-like symptoms, making visual diagnosis challenging due to overlap with other dermatological conditions. Existing AI-based studies on monkeypox classification have largely relied on Convolutional Neural Networks (CNNs), with limited exploration of Transformer architectures or robust interpretability frameworks. Moreover, most explainability research still depends on conventional heatmap techniques without systematic evaluation. This study addresses these gaps by applying Transformer-based models and introducing a novel hybrid explainability approach. Methods: We fine-tuned Vision Transformer (ViT) and Data-Efficient Image Transformer (DeiT) models for both binary and multi-class classification of monkeypox and other skin lesions. To improve interpretability, we integrated multiple explainable AI techniques—Gradient-weighted Class Activation Mapping (Grad-CAM), Layer-wise Relevance Propagation (LRP), and Attention Rollout (AR)—and proposed a hybrid method that combines these heatmaps using Principal Component Analysis (PCA). The reliability of explanations was quantitatively assessed using deletion and insertion metrics. Results: ViT achieved superior performance with an AUC of 0.9192 in binary classification and 0.9784 in multi-class tasks, outperforming DeiT. The hybrid approach (Grad-CAM + LRP) produced the most informative explanations, achieving higher insertion scores and lower deletion scores than individual methods, thereby enhancing clinical reliability. Conclusions: This study is among the first to combine Transformer models with systematically evaluated hybrid explainability techniques for monkeypox classification. By improving both predictive performance and interpretability, our framework contributes to more transparent and clinically relevant AI applications in dermatology. Future work should expand datasets and integrate clinical metadata to further improve generalizability.

Pix2Pix generative-adversarial network in improving the quality of T2-weighted prostate magnetic resonance imaging: a multi-reader study - Diagnostic and Interventional Radiology

Otitis media is a major cause of hearing loss, particularly in children. However, nonspecific symptoms and subjective evaluations make its diagnosis challenging. To address this, we developed transformer-based models to classify tympanic membrane conditions from otoscopic images. This approach aims to enhance diagnostic transparency and reliability in clinical settings. We trained vision transformer (ViT) and Data-efficient Image Transformer (DeiT) models on 454 pediatric and adult otoscopic images. These models performed multi-class classification to distinguish between normal, effusion, and tube conditions. For explainability, we utilized Gradient-weighted Class Activation Map (Grad-CAM), Layer-wise Relevance Propagation (LRP), and Attention Rollout (AR). Furthermore, we introduced a hybrid fusion strategy based on Canonical Correlation Analysis. The framework’s effectiveness was then evaluated using insertion and deletion causal metrics. The ViT model achieved an accuracy of 97.78% (AUC: 0.998), outperforming DeiT, which reached 93.33% (AUC: 0.994). Notably, ViT attained an F1-score of 97.30% for the effusion class. Among the Explainable Artificial Intelligence (AI) methods, the hybrid LRP and AR approach provided the highest explainability. It yielded an average deletion score of 0.3008 and an insertion score of 0.8918, precisely highlighting critical image features for model predictions. In conclusion, integrating transformer-based models with hybrid explainability methods significantly enhances diagnostic transparency. These advancements foster clinician trust and lay a strong foundation for reliable clinical decision support systems.