Deep learning has achieved remarkable success in chest x-ray interpretation, yet most models remain black boxes, producing accurate predictions without exposing the clinical reasoning behind them. This opacity limits trust and adoption in real-world practice. We introduce Med-ViX-Ray, a knowledge-guided and interpretable framework that integrates symbolic clinical reasoning into a vision Transformer backbone. The model leverages a structured graph of radiological signs and conditions, aligning image attention maps with domain knowledge through a probabilistic soft-matching module and a nudging mechanism that refines classifier outputs. This dual integration allows predictions to be explained in terms of clinically meaningful signs and corresponding image regions, offering transparency beyond post-hoc heatmaps. We evaluated Med-ViX-Ray on MIMIC-CXR for training and internal validation, and tested its generalization on VinDR-CXR and RSNA Pneumonia benchmarks. The proposed method improves recall and F1-score compared to a strong SwinV2 baseline (Respectively, F1-micro: 0.561 - 0.456; Precision: 0.462 - 0-529; Recall: 0.715 - 0.466; ROC: 0.788 - 0.744), while maintaining competitive overall performance. Qualitative analyses confirm that the model highlights clinically relevant regions and sign-activations aligned with radiological practice. These results suggest that knowledge-guided attention and sign-based explanations can enhance interpretability and recall in chest X-ray classification models. Future work will extend the framework toward report generation and prospective clinical evaluation.

Colorectal cancer is the second leading cause of cancer-related mortality worldwide, posing a substantial burden on healthcare systems. Identifying DNA mismatch repair deficiency (dMMR) is critical for guiding treatment, yet conventional methods rely on labor-intensive DNA analysis. While deep-learning approaches have shown promise for predicting dMMR from histopathological images, most studies focus exclusively on tumor regions and single-scale representations. This study systematically evaluates the predictive value of tumor and non-tumor regions across multiple magnifications for dMMR prediction from whole-slide images (WSIs). A total of 24 different modeling approaches were evaluated, varying by tissue origin (tumor vs. non-tumor), magnification level (5x and 20x), and tile embedding strategy, including digital pathology foundation models. Tile embeddings were further trained with 1228 WSIs using multiple-instance learning (MIL) based approach. The best-performing configurations were selected for external evaluation. External testing was carried out on two independent cohorts consisting of 1010 and 457 WSIs, respectively. Non-tumorous regions demonstrated measurable predictive value, although performance remained lower than that obtained from tumor regions (F1 = 0.896, precision = 0.888, sensitivity = 0.594, specificity = 0.982). Among the nine models selected during internal validation, the top three models-one multi-scale approach and two models trained on 20x tumor regions-achieved F1 scores of 0.870-0.889 with precision of 0.885-0.920, sensitivity of 0.852, and specificity of 0.889-0.926. On external validation, the top three models, all based on foundation-model tile embeddings, achieved F1 scores of 0.916-0.919 on the first cohort and 0.928-0.934 on the second cohort. Across cohorts, specificity remained consistently high (0.964-0.992), while sensitivity ranged from 0.500 to 0.682. This study demonstrates that dMMR status in colorectal cancer can be effectively predicted from histopathological WSIs using MIL-based models, with moderate generalizability across independent cohorts. In addition to confirming the predictive value of tumor regions, the results reveal that non-tumorous tissue also contains detectable predictive signals, suggesting that microenvironmental features may contribute to dMMR-associated histological patterns. Furthermore, the use of foundation model-derived embeddings improved generalizability across datasets. Future work should explore integrating non-tumor tissue features and clinical data to further improve predictive performance.

Patient-ventilator interaction critically influences the safety and effectiveness of mechanical ventilation, particularly under support modes such as pressure support ventilation (PSV). This study introduces a mathematical model of the respiratory center that integrates chemical feedback, mechanical feedback, and reflex mechanisms to simulate spontaneous breathing and its interaction with a mechanical ventilator. The model incorporates O2 and CO2 chemoreflexes, mechanical feedback, and neural control circuits. Simulation studies evaluated the model's performance under hypercapnia, hypoxia, varying PSV levels, and different types of patient-ventilator asynchrony, comparing outcomes to published literature data. The model accurately reproduced respiratory rate and tidal volume responses with mean absolute percentage errors below 12%, and generated asynchrony frequencies within a 5% acceptance margin. Characteristic ventilatory patterns and emergent asynchronies appeared naturally, without explicit programming of events, demonstrating the physiological plausibility of the model. This respiratory center model provides a robust tool for investigating respiratory control, testing ventilator strategies, and supporting the development of intelligent ventilation approaches, including in silico clinical trials. Its ability to reproduce both normal and pathological ventilatory patterns underscores its potential for translational and clinical research applications.

Generative Artificial Intelligence (GAI) offers promising solutions to long-standing challenges in developing medical imaging methods and applications, including data scarcity, privacy concerns, and class imbalance. However, limited consolidation of publicly accessible synthetic datasets and trained GAI checkpoints restricts reproducibility and benchmarking. This systematic review aims to identify and evaluate such resources and assess their utility in clinical imaging applications. We systematically searched PubMed, IEEE Xplore, and Scopus for studies published between January 2017 and June 2024. Eligible studies generated or used synthetic medical image datasets and publicly released either the dataset or the trained GAI model. Extracted data included imaging modality, dataset characteristics, model architecture, public availability, and evaluation strategy. Of 941 screened records, 35 studies met inclusion criteria, comprising 37 publicly available resources spanning radiology (59%), pathology (16%), ophthalmology (14%), and dermatology (11%). Generative models included generative adversarial networks (73%), diffusion models (21%), autoencoders (3%), and hybrid architectures (8%). As some studies employed multiple model types, these categories are not mutually exclusive. Fifteen (43%) studies provided trained model checkpoints, enabling the generation of task-specific synthetic data. Evaluation methods included quantitative metrics, clinical expert assessment, and downstream performance in classification, segmentation, or detection tasks. Although the reviewed resources support diverse downstream applications, publicly available synthetic datasets and trained models remain scarce. Evaluation strategies vary widely, and the absence of standardized benchmarks limits cross-study comparisons and reliability assessment. To support reproducibility and responsible use of GAI in medical imaging, future work should prioritize the public release of curated synthetic resources, clearer guidance on model selection, and standardized, multi-dimensional evaluation frameworks.