<b>Background:</b> Chest X-rays are rapidly gaining prominence as a prevalent diagnostic tool, as recognized by the World Health Organization (WHO). However, interpreting chest X-rays can be demanding and time-consuming, even for experienced radiologists, leading to potential misinterpretations and delays in treatment. <b>Method:</b> The purpose of this research is the development of a RadAI model. The RadAI model can accurately detect four types of lung abnormalities in chest X-rays and generate a report on each identified abnormality. Moreover, deep learning algorithms, particularly convolutional neural networks (CNNs), have demonstrated remarkable potential in automating medical image analysis, including chest X-rays. This work addresses the challenge of chest X-ray interpretation by fine tuning the following three advanced deep learning models: Feature-selective and Spatial Receptive Fields Network (FSRFNet50), ResNext50, and ResNet50. These models are compared based on accuracy, precision, recall, and F1-score. <b>Results:</b> The outstanding performance of RadAI shows its potential to assist radiologists to interpret the detected chest abnormalities accurately. <b>Conclusions:</b> RadAI is beneficial in enhancing the accuracy and efficiency of chest X-ray interpretation, ultimately supporting the timely and reliable diagnosis of lung abnormalities.

To determine the performance of a commercially available AI tool for fracture detection when used in children with osteogenesis imperfecta (OI). All appendicular and pelvic radiographs from an OI clinic at a single centre from 48 patients were included. Seven radiologists evaluated anonymised images in two rounds, first without, then with AI assistance. Differences in diagnostic accuracy between the rounds were analysed. 48 patients (mean 12 years) provided 336 images, containing 206 fractures established by consensus opinion of two radiologists. AI produced a per-examination accuracy of 74.8% [95% CI: 65.4%, 82.7%], compared to average radiologist performance at 83.4% [95% CI: 75.2%, 89.8%]. Radiologists using AI assistance improved average radiologist accuracy per examination to 90.7% [95% CI: 83.5%, 95.4%]. AI gave more false negatives than radiologists, with 80 missed fractures versus 41, respectively. Radiologists were more likely (74.6%) to alter their original decision to agree with AI at the per-image level, 82.8% of which led to a correct result, 64.0% of which were changing from a false positive to a true negative. Despite inferior standalone performance, AI assistance can still improve radiologist fracture detection in a rare disease paediatric population. Radiologists using AI typically led to more accurate diagnostic outcomes through reduced false positives. Future studies focusing on the real-world application of AI tools in a larger population of children with bone fragility disorders will help better evaluate whether these improvements in accuracy translate into improved patient outcomes. Question How well does a commercially available artificial intelligence (AI) tool identify fractures, on appendicular radiographs of children with osteogenesis imperfecta (OI), and can it also improve radiologists' identification of fractures in this population? Findings Specialist human radiologists outperformed the AI fracture detection tool when acting alone; however, their diagnostic performance overall improved with AI assistance. Clinical relevance AI assistance improves specialist radiologist fracture detection in children with osteogenesis imperfecta, even with AI performance alone inferior to the radiologists acting alone. The reason for this was due to the AI moderating the number of false positives generated by the radiologists.

X-ray imaging is a rapid and cost-effective tool for visualizing internal human anatomy. While multi-view X-ray imaging provides complementary information that enhances diagnosis, intervention, and education, acquiring images from multiple angles increases radiation exposure and complicates clinical workflows. To address these challenges, we propose a novel view-conditioned diffusion model for synthesizing multi-view X-ray images from a single view. Unlike prior methods, which are limited in angular range, resolution, and image quality, our approach leverages the Diffusion Transformer to preserve fine details and employs a weak-to-strong training strategy for stable high-resolution image generation. Experimental results demonstrate that our method generates higher-resolution outputs with improved control over viewing angles. This capability has significant implications not only for clinical applications but also for medical education and data extension, enabling the creation of diverse, high-quality datasets for training and analysis. Our code is available at GitHub.

Artificial intelligence (AI) has significant potential in healthcare applications, but its training and deployment faces challenges due to healthcare's diverse data, complex tasks, and the need to preserve privacy. Foundation models that perform well on medical tasks and require less task-specific tuning data are critical to accelerate the development of healthcare AI applications. We introduce MedGemma, a collection of medical vision-language foundation models based on Gemma 3 4B and 27B. MedGemma demonstrates advanced medical understanding and reasoning on images and text, significantly exceeding the performance of similar-sized generative models and approaching the performance of task-specific models, while maintaining the general capabilities of the Gemma 3 base models. For out-of-distribution tasks, MedGemma achieves 2.6-10% improvement on medical multimodal question answering, 15.5-18.1% improvement on chest X-ray finding classification, and 10.8% improvement on agentic evaluations compared to the base models. Fine-tuning MedGemma further improves performance in subdomains, reducing errors in electronic health record information retrieval by 50% and reaching comparable performance to existing specialized state-of-the-art methods for pneumothorax classification and histopathology patch classification. We additionally introduce MedSigLIP, a medically-tuned vision encoder derived from SigLIP. MedSigLIP powers the visual understanding capabilities of MedGemma and as an encoder achieves comparable or better performance than specialized medical image encoders. Taken together, the MedGemma collection provides a strong foundation of medical image and text capabilities, with potential to significantly accelerate medical research and development of downstream applications. The MedGemma collection, including tutorials and model weights, can be found at https://goo.gle/medgemma.

Osteoporosis is a chronic disease characterized by a progressive decline in bone density and quality, leading to increased bone fragility and a higher susceptibility to fractures, even in response to minimal trauma. Osteoporotic fractures represent a major source of morbidity and mortality among postmenopausal women. This condition poses both clinical and societal challenges, as its consequences include a significant reduction in quality of life, prolonged dependency, and a substantial increase in healthcare costs. Therefore, the development of reliable tools for predicting fracture risk is essential for the effective management of affected patients. In this study, we developed a predictive model based on the Random Forest (RF) algorithm for risk stratification of fragility fractures, integrating clinical, demographic, and imaging variables derived from dual-energy X-ray absorptiometry (DXA) and 3D modeling. Two independent cohorts were analyzed: the HURH cohort and the Camargo cohort, enabling both internal and external validation of the model. The results showed that the RF model consistently outperformed other classification algorithms, including k-nearest neighbors (KNN), support vector machines (SVM), decision trees (DT), and Gaussian naive Bayes (GNB), demonstrating high accuracy, sensitivity, specificity, area under the ROC curve (AUC), and Matthews correlation coefficient (MCC). Additionally, variable importance analysis highlighted that previous fracture history, parathyroid hormone (PTH) levels, and lumbar spine T-score, along with other densitometric parameters, were key predictors of fracture risk. These findings suggest that the integration of advanced machine learning techniques with clinical and imaging data can optimize early identification of high-risk patients, enabling personalized preventive strategies and improving the clinical management of osteoporosis.

Radiology report generation represents a significant application within medical AI, and has achieved impressive results. Concurrently, large language models (LLMs) have demonstrated remarkable performance across various domains. However, empirical validation indicates that general LLMs tend to focus more on linguistic fluency rather than clinical effectiveness, and lack the ability to effectively capture the relationship between X-ray images and their corresponding texts, thus resulting in poor clinical practicability. To address these challenges, we propose Optimal Transport-Driven Radiology Report Generation (OTDRG), a novel framework that leverages Optimal Transport (OT) to align image features with disease labels extracted from reports, effectively bridging the cross-modal gap. The core component of OTDRG is Alignment \& Fine-Tuning, where OT utilizes results from the encoding of label features and image visual features to minimize cross-modal distances, then integrating image and text features for LLMs fine-tuning. Additionally, we design a novel disease prediction module to predict disease labels contained in X-ray images during validation and testing. Evaluated on the MIMIC-CXR and IU X-Ray datasets, OTDRG achieves state-of-the-art performance in both natural language generation (NLG) and clinical efficacy (CE) metrics, delivering reports that are not only linguistically coherent but also clinically accurate.

Knee osteoarthritis (KOA) is a progressive degenerative joint disease and a leading cause of disability worldwide. Manual diagnosis of KOA from X-ray images is subjective and prone to inter- and intra-observer variability, making early detection challenging. While deep learning (DL)-based models offer automation, they often require large labeled datasets, lack interpretability, and do not provide quantitative feature measurements. Our study presents an automated KOA severity assessment system that integrates a pretrained DL model with image processing techniques to extract and quantify key KOA imaging biomarkers. The pipeline includes contrast limited adaptive histogram equalization (CLAHE) for contrast enhancement, DexiNed-based edge extraction, and thresholding for noise reduction. We design customized algorithms that automatically detect and quantify joint space narrowing (JSN) and osteophytes from the extracted edges. The proposed model quantitatively assesses JSN and finds the number of intercondylar osteophytes, contributing to severity classification. The system achieves accuracies of 88% for JSN detection, 80% for osteophyte identification, and 73% for KOA classification. Its key strength lies in eliminating the need for any expensive training process and, consequently, the dependency on labeled data except for validation. Additionally, it provides quantitative data that can support classification in other OA grading frameworks.

Early and accurate detection of oral cancer plays a pivotal role in improving patient outcomes. This research introduces a custom-designed, 19-layer convolutional neural network (CNN) for the automated diagnosis of oral cancer using clinical images of the lips and tongue. The methodology integrates advanced preprocessing steps, including min-max normalization and histogram-based contrast enhancement, to optimize image features critical for reliable classification. The model is extensively validated on the publicly available Oral Cancer (Lips and Tongue) Images (OCI) dataset, which is divided into 80% training and 20% testing subsets. Comprehensive performance evaluation employs established metrics-accuracy, sensitivity, specificity, precision, and F1-score. Our CNN architecture achieved an accuracy of 99.54%, sensitivity of 95.73%, specificity of 96.21%, precision of 96.34%, and F1-score of 96.03%, demonstrating substantial improvements over prominent transfer learning benchmarks, including SqueezeNet, AlexNet, Inception, VGG19, and ResNet50, all tested under identical experimental protocols. The model's robust performance, efficient computation, and high reliability underline its practicality for clinical application and support its superiority over existing approaches. This study provides a reproducible pipeline and a new reference point for deep learning-based oral cancer detection, facilitating translation into real-world healthcare environments and promising enhanced diagnostic confidence.

To develop an interpretable machine learning model, explained using SHAP, based on imaging features of adolescent idiopathic scoliosis extracted by convolutional neural networks (CNNs), in order to predict the risk of curve progression and identify the most accurate predictive model. This study included 233 patients with adolescent idiopathic scoliosis from three medical centers. CNNs were used to extract features from full-spine coronal X-ray images taken at three follow-up points for each patient. Imaging and clinical features from center 1 were analyzed using the Boruta algorithm to identify independent predictors. Data from center 1 were divided into training (80%) and testing (20%) sets, while data from centers 2 and 3 were used as external validation sets. Six machine learning models were constructed. Receiver operating characteristic (ROC) curves were plotted, and model performance was assessed by calculating the area under the curve (AUC), accuracy, sensitivity, and specificity in the training, testing, and external validation sets. The SHAP interpreter was used to analyze the most effective model. The six models yielded AUCs ranging from 0.565 to 0.989, accuracies from 0.600 to 0.968, sensitivities from 0.625 to 1.0, and specificities from 0.571 to 0.974. The XGBoost model achieved the best performance, with an AUC of 0.896 in the external validation set. SHAP analysis identified the change in the main Cobb angle between the second and first follow-ups [Cobb1(2−1)] as the most important predictor, followed by the main Cobb angle at the second follow-up (Cobb1-2) and the change in the secondary Cobb angle [Cobb2(2−1)]. The XGBoost model demonstrated the best predictive performance in the external validation cohort, confirming its preliminary stability and generalizability. SHAP analysis indicated that Cobb1(2−1) was the most important feature for predicting scoliosis progression. This model offers a valuable tool for clinical decision-making by enabling early identification of high-risk patients and supporting early intervention strategies through automated feature extraction and interpretable analysis. The online version contains supplementary material available at 10.1186/s12891-025-08841-3.

This study aims to enhance the accuracy of pneumonia diagnosis from X-ray images by developing a model that integrates Vision Transformer (ViT) and Graph Convolutional Networks (GCN) for improved feature extraction and diagnostic performance. The ViT-GCN model was designed to leverage the strengths of both ViT, which captures global image information by dividing the image into fixed-size patches and processing them in sequence, and GCN, which captures node features and relationships through message passing and aggregation in graph data. A composite loss function combining multivariate cross-entropy, focal loss, and GHM loss was introduced to address dataset imbalance and improve training efficiency on small datasets. The ViT-GCN model demonstrated superior performance, achieving an accuracy of 91.43\% on the COVID-19 chest X-ray database, surpassing existing models in diagnostic accuracy for pneumonia. The study highlights the effectiveness of combining ViT and GCN architectures in medical image diagnosis, particularly in addressing challenges related to small datasets. This approach can lead to more accurate and efficient pneumonia diagnoses, especially in resource-constrained settings where small datasets are common.