This study aimed to systematically review the current performance of large language models (LLMs) in dento-maxillofacial radiology (DMFR). Five electronic databases were used to identify studies that developed, fine-tuned, or evaluated LLMs for DMFR-related tasks. Data extracted included study purpose, LLM type, images/text source, applied language, dataset characteristics, input and output, performance outcomes, evaluation methods, and reference standards. Customized assessment criteria adapted from the TRIPOD-LLM reporting guideline were used to evaluate the risk-of-bias in the included studies specifically regarding the clarity of dataset origin, the robustness of performance evaluation methods, and the validity of the reference standards. The initial search yielded 1621 titles, and nineteen studies were included. These studies investigated the use of LLMs for tasks including the production and answering of DMFR-related qualification exams and educational questions (n = 8), diagnosis and treatment recommendations (n = 7), and radiology report generation and patient communication (n = 4). LLMs demonstrated varied performance in diagnosing dental conditions, with accuracy ranging from 37-92.5% and expert ratings for differential diagnosis and treatment planning between 3.6-4.7 on a 5-point scale. For DMFR-related qualification exams and board-style questions, LLMs achieved correctness rates between 33.3-86.1%. Automated radiology report generation showed moderate performance with accuracy ranging from 70.4-81.3%. LLMs demonstrate promising potential in DMFR, particularly for diagnostic, educational, and report generation tasks. However, their current accuracy, completeness, and consistency remain variable. Further development, validation, and standardization are needed before LLMs can be reliably integrated as supportive tools in clinical workflows and educational settings.

Radiology report generation (RRG) tasks leverage computer-aided technology to automatically produce descriptive text reports for medical images, aiming to ease radiologists' workload, reduce misdiagnosis rates, and lessen the pressure on medical resources. However, previous works have yet to focus on enhancing feature extraction of low-quality images, incorporating cross-modal interaction information, and mitigating latency in report generation. We propose an Image-Enhanced Cross-Modal Fusion Network (IFNet) for automatic RRG to tackle these challenges. IFNet includes three key components. First, the image enhancement module enhances the detailed representation of typical and atypical structures in X-ray images, thereby boosting detection success rates. Second, the cross-modal fusion networks efficiently and comprehensively capture the interactions of cross-modal features. Finally, a more efficient transformer report generation module is designed to optimize report generation efficiency while being suitable for low-resource devices. Experimental results on public datasets IU X-ray and MIMIC-CXR demonstrate that IFNet significantly outperforms the current state-of-the-art methods.

Pulmonary disease can severely impair respiratory function and be life-threatening. Accurately recognizing pulmonary diseases in chest X-ray images is challenging due to overlapping body structures and the complex anatomy of the chest. We propose an adaptive multiscale feature fusion model for recognizing Chest X-ray images of pneumonia, tuberculosis, and COVID-19, which are common pulmonary diseases. We introduce an Adaptive Multiscale Fusion Network (AMFNet) for pulmonary disease classification in chest X-ray images. AMFNet consists of a lightweight Multiscale Fusion Network (MFNet) and ResNet50 as the secondary feature extraction network. MFNet employs Fusion Blocks with self-calibrated convolution (SCConv) and Attention Feature Fusion (AFF) to capture multiscale semantic features, and integrates a custom activation function, MFReLU, which is employed to reduce the model's memory access time. A fusion module adaptively combines features from both networks. Experimental results show that AMFNet achieves 97.48% accuracy and an F1 score of 0.9781 on public datasets, outperforming models like ResNet50, DenseNet121, ConvNeXt-Tiny, and Vision Transformer while using fewer parameters.

Generative image models have achieved remarkable progress in both natural and medical imaging. In the medical context, these techniques offer a potential solution to data scarcity-especially for low-prevalence anomalies that impair the performance of AI-driven diagnostic and segmentation tools. However, questions remain regarding the fidelity and clinical utility of synthetic images, since poor generation quality can undermine model generalizability and trust. In this study, we evaluate the effectiveness of state-of-the-art generative models-Generative Adversarial Networks (GANs) and Diffusion Models (DMs)-for synthesizing chest X-rays conditioned on four abnormalities: Atelectasis (AT), Lung Opacity (LO), Pleural Effusion (PE), and Enlarged Cardiac Silhouette (ECS). Using a benchmark composed of real images from the MIMIC-CXR dataset and synthetic images from both GANs and DMs, we conducted a reader study with three radiologists of varied experience. Participants were asked to distinguish real from synthetic images and assess the consistency between visual features and the target abnormality. Our results show that while DMs generate more visually realistic images overall, GANs can report better accuracy for specific conditions, such as absence of ECS. We further identify visual cues radiologists use to detect synthetic images, offering insights into the perceptual gaps in current models. These findings underscore the complementary strengths of GANs and DMs and point to the need for further refinement to ensure generative models can reliably augment training datasets for AI diagnostic systems.

Although the use of artificial intelligence (AI) in healthcare is increasing, stakeholder engagement remains poor, particularly relating to understanding parent/carer acceptance of AI tools in paediatric imaging. We explore these perceptions and compare them to the opinions of children and young people (CYAP). A UK national online survey was conducted, inviting parents, carers and guardians of children to participate. The survey was "live" from June 2022 to 2023. The survey included questions asking about respondents' views of AI in general, as well as in specific circumstances (e.g. fractures) with respect to children's healthcare. One hundred forty-six parents/carers (mean age = 45; range = 21-80) from all four nations of the UK responded. Most respondents (93/146, 64%) believed that AI would be more accurate at interpreting paediatric musculoskeletal radiographs than healthcare professionals, but had a strong preference for human supervision (66%). Whilst male respondents were more likely to believe that AI would be more accurate (55/72, 76%), they were twice as likely as female parents/carers to believe that AI use could result in their child's data falling into the wrong hands. Most respondents would like to be asked permission before AI is used for the interpretation of their child's scans (104/146, 71%). Notably, 79% of parents/carers prioritised accuracy over speed compared to 66% of CYAP. Parents/carers feel positively about AI for paediatric imaging but strongly discourage autonomous use. Acknowledging the diverse opinions of the patient population is vital in aiding the successful integration of AI for paediatric imaging. Parents/carers demonstrate a preference for AI use with human supervision that prioritises accuracy, transparency and institutional accountability. AI is welcomed as a supportive tool, but not as a substitute for human expertise. Parents/carers are accepting of AI use, with human supervision. Over half believe AI would replace doctors/nurses looking at bone X-rays within 5 years. Parents/carers are more likely than CYAP to trust AI's accuracy. Parents/carers are also more sceptical about AI data misuse.

To assess how artificial intelligence (AI) models perform in evaluating bone quality and quantity in the preoperative planning process for dental implants. This review included studies that utilized AI-based assessments of bone quality and/or quantity based on radiographic images in the preoperative phase. Studies published in English before April 2025 were used in this review, which were obtained from searches in PubMed/MEDLINE, Embase, Web of Science, Scopus, and the Cochrane Library, as well as from manual searches. Eleven studies met the inclusion criteria. Five studies focused on bone quality evaluation and six studies included volumetric assessments using AI models. The performance measures included accuracy, sensitivity, specificity, precision, F1 score, and Dice coefficient, and were compared with human expert evaluations. AI models demonstrated high accuracy (76.2%-99.84%), high sensitivity (78.9%-100%), and high specificity (66.2%-99%). AI models have potential for the evaluation of bone quality and quantity, although standardization and external validation studies are lacking. Future studies should propose multicenter datasets, integration into clinical workflows, and the development of refined models to better reflect real-life conditions. AI has the potential to offer clinicians with reliable automated evaluations of bone quality and quantity, with the promise of a fully automated system of implant planning. It may also support preoperative workflows for clinical decision-making based on evidence more efficiently.

Chronological age is an important component of medical risk scores and decision-making. However, there is considerable variability in how individuals age. We recently published an open-source deep learning model to assess biological age from chest radiographs (CXR-Age), which predicts all-cause and cardiovascular mortality better than chronological age. Here, we compare CXR-Age to two established epigenetic aging clocks (First generation-Horvath Age; Second generation-DNAm PhenoAge) to test which is more strongly associated with cardiopulmonary disease and frailty. Our cohort consisted of 2,097 participants from the Project Baseline Health Study, a prospective cohort study of individuals from four US sites. We compared the association between the different aging clocks and measures of cardiopulmonary disease, frailty, and protein abundance collected at the participant's first annual visit using linear regression models adjusted for common confounders. We found that CXR-Age was associated with coronary calcium, cardiovascular risk factors, worsening pulmonary function, increased frailty, and abundance in plasma of two proteins implicated in neuroinflammation and aging. Associations with DNAm PhenoAge were weaker for pulmonary function and all metrics in middle-age adults. We identified thirteen proteins that were associated with DNAm PhenoAge, one (CDH13) of which was also associated with CXR-Age. No associations were found with Horvath Age. These results suggest that CXR-Age may serve as a better metric of cardiopulmonary aging than epigenetic aging clocks, especially in midlife adults.

By carefully analyzing latent image properties, content-based image retrieval (CBIR) systems are able to recover pertinent images without relying on text descriptions, natural language tags, or keywords related to the image. This search procedure makes it quite easy to automatically retrieve images in huge, well-balanced datasets. However, in the medical field, such datasets are usually not available. This study proposed an advanced DL technique to enhance the accuracy of image retrieval in complex medical datasets. The proposed model can be integrated into five stages, namely pre-processing, decomposing the images, feature extraction, dimensionality reduction, and classification with an image retrieval mechanism. The hybridized Wavelet-Hadamard Transform (HWHT) was utilized to obtain both low and high frequency detail for analysis. In order to extract the main characteristics, the Gray Level Co-occurrence Matrix (GLCM) was employed. Furthermore, to minimize feature complexity, Sine chaos based artificial rabbit optimization (SCARO) was utilized. By employing the Bhattacharyya Coefficient for improved similarity matching, the Bhattacharya Context performance aware global attention-based Transformer (BCGAT) improves classification accuracy. The experimental results proved that the COVID-19 Chest X-ray image dataset attained higher accuracy, precision, recall, and F1-Score of 99.5%, 97.1%, 97.1%, and 97.1%, 97.1%, respectively. However, the chest x-ray image (pneumonia) dataset has attained higher accuracy, precision, recall, and F1-score values of 98.60%, 98.49%, 97.40%, and 98.50%, respectively. For the NIH chest X-ray dataset, the accuracy value is 99.67%.

Cardiomyopathy is a life-threatening condition associated with heart failure, arrhythmias, thromboembolism, and sudden cardiac death, posing a significant contribution to worldwide morbidity and mortality. Cardiomegaly, which is usually the initial radiologic sign, may reflect the progression of an underlying heart disease or an underlying undiagnosed cardiac condition. Chest radiography is the most frequently used imaging method for detecting heart enlargement. Prompt and accurate diagnosis is essential for prompt intervention and appropriate treatment planning to prevent disease progression and improve patient outcomes. The current work provides a new methodology for automated cardiomegaly diagnosis using X-ray images through the fusion of Block-Matching and 3D Filtering (BM3D) within the Ensemble Hilbert-Huang Transform (EHHT), convolutional neural networks like Pretrained VGG16, ResNet50, InceptionV3, DenseNet169, and Spiking Neural Networks (SNN), and Classifiers. BM3D is first used for image edge retention and noise reduction, and then EHHT is applied to obtain informative features from X-ray images. The features that have been extracted are then processed using an SNN that simulates neural processes at a biological level and offers a biologically possible classification solution. Gradient-weighted Class Activation Mapping (GradCAM) emphasized important areas that affected model predictions. The SNN performed the best among all the models tested, with 97.6 % accuracy, 96.3 % sensitivity, and 98.2 % specificity. These findings show the SNN's high potential for facilitating accurate and efficient cardiomyopathy diagnosis, leading to enhanced clinical decision-making and patient outcomes.

Data binary encoding has proven to be a versatile tool for optimizing data processing and memory efficiency in various machine learning applications. This includes deep barcoding, generating barcodes from deep learning feature extraction for image retrieval of similar cases among millions of indexed images. Despite the recent advancement in barcode generation methods, converting high-dimensional feature vectors (e.g., deep features) to compact and discriminative binary barcodes is still an urgent necessity and remains an unresolved problem. Difference-based binarization of features is one of the most efficient binarization methods, transforming continuous feature vectors into binary sequences and capturing trend information. However, the performance of this method is highly dependent on the ordering of the input features, leading to a significant combinatorial challenge. This research addresses this problem by optimizing feature sequences based on retrieval performance metrics. Our approach identifies optimal feature orderings, leading to substantial improvements in retrieval effectiveness compared to arbitrary or default orderings. We assess the performance of the proposed approach in various medical and non-medical image retrieval tasks. This evaluation includes medical images from The Cancer Genome Atlas (TCGA), a comprehensive publicly available dataset, as well as COVID-19 Chest X-rays dataset. In addition, we evaluate the proposed approach on non-medical benchmark image datasets, such as CIFAR-10, CIFAR-100, and Fashion-MNIST. Our findings demonstrate the importance of optimizing binary barcode representation to significantly enhance accuracy for fast image retrieval across a wide range of applications, highlighting the applicability and potential of barcodes in various domains.