Large Language Models (LLMs) show promise for generating patient-friendly radiology reports, but the performance of open-source versus proprietary LLMs needs assessment. To compare open-source and proprietary LLMs in generating patient-friendly radiology reports from chest CTs using quantitative readability metrics and qualitative assessments by radiologists. Fifty chest CT reports were processed by seven LLMs: three open-source models (Llama-3-70b, Mistral-7b, Mixtral-8x7b) and four proprietary models (GPT-4, GPT-3.5-Turbo, Claude-3-Opus, Gemini-Ultra). Simplification was evaluated using five quantitative readability metrics. Three radiologists rated patient-friendliness on a five-point Likert scale across five criteria. Content and coherence errors were counted. Inter-rater reliability and differences among models were statistically assessed. Inter-rater reliability was substantial to near perfect (κ = 0.76-0.86). Qualitatively, Llama-3-70b was non-inferior to leading proprietary models in 4/5 categories. GPT-3.5-Turbo showed the best overall readability, outperforming GPT-4 in two metrics. Llama-3-70b outperformed GPT-3.5-Turbo on the CLI (p = 0.006). Claude-3-Opus and Gemini-Ultra scored lower on readability but were rated highly in qualitative assessments. Claude-3-Opus maintained perfect factual accuracy. Claude-3-Opus and GPT-4 outperformed Llama-3-70b in emotional sensitivity (90.0 % vs 46.0 %, p < 0.001). Llama-3-70b shows strong potential in generating quality, patient-friendly radiology reports, challenging proprietary models. With further adaptation, open-source LLMs could advance patient-friendly reporting technology.

Chronic obstructive pulmonary disease (COPD) is a highly heterogeneous condition characterized by diverse pulmonary and extrapulmonary manifestations. Efforts to quantify its various components using CT imaging have advanced, aiming for more precise, objective, and reproducible assessment and management. Beyond emphysema and small airway disease, the two major components of COPD, CT quantification enables the evaluation of pulmonary vascular alteration, ventilation-perfusion mismatches, fissure completeness, and extrapulmonary features such as altered body composition, osteoporosis, and atherosclerosis. Recent advancements, including the application of deep learning techniques, have facilitated fully automated segmentation and quantification of CT parameters, while innovations such as image standardization hold promise for enhancing clinical applicability. Numerous studies have reported associations between quantitative CT parameters and clinical or physiologic outcomes in patients with COPD. However, barriers remain to the routine implementation of these technologies in clinical practice. This review highlights recent research on COPD quantification, explores advances in technology, and also discusses current challenges and potential solutions for improving quantification methods.

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.

Pneumonia, as a serious respiratory disease caused by bacterial, viral or fungal infections, is an important cause of increased morbidity and mortality in high-risk populations (e.g.the elderly, infants and young children, and immunodeficient patients) worldwide. Early diagnosis is decisive for improving patient prognosis. In this study, we propose a Dual-Branch Attention Fusion Network based on transfer learning, aiming to improve the accuracy of pneumonia classification in lung X-ray images. The model adopts a dual-branch feature extraction architecture: independent feature extraction paths are constructed based on pre-trained convolutional neural networks (CNNs) and structural spatial state models, respectively, and feature complementarity is achieved through a feature fusion strategy. In the fusion stage, a Self-Attention Mechanism is introduced to dynamically weight the feature representations of different paths, which effectively improves the characterisation of key lesion regions. The experiments are carried out based on the publicly available ChestX-ray dataset, and through data enhancement, migration learning optimisation and hyper-parameter tuning, the model achieves an accuracy of 97.78% on an independent test set, and the experimental results fully demonstrate the excellent performance of the model in the field of pneumonia diagnosis, which provides a new and powerful tool for the rapid and accurate diagnosis of pneumonia in clinical practice, and our methodology provides a high--performance computational framework for intelligent pneumonia Early screening provides a high-performance computing framework, and its architecture design of multipath and attention fusion can provide a methodological reference for other medical image analysis tasks.&#xD.

Hyperpolarized ¹²⁹Xenon magnetic resonance imaging (MRI) measures the extent of lung ventilation by ventilation defect percent (VDP), but VDP alone cannot distinguish between diseases. Prior studies have reported anecdotal evidence of disease-specific defect patterns such as wedge-shaped defects in asthma and polka-dot defects in lymphangioleiomyomatosis (LAM). Neural network artificial intelligence can evaluate image shapes and textures to classify images, but this has not been attempted in xenon MRI. We hypothesized that an artificial intelligence network trained on ventilation MRI could classify diseases based on spatial patterns in lung MR images alone. Xenon MRI data in six pulmonary conditions (control, asthma, bronchiolitis obliterans syndrome, bronchopulmonary dysplasia, cystic fibrosis, LAM) were used to train convolutional neural networks. Network performance was assessed with top-1 and top-2 accuracy, recall, precision, and one-versus-all area under the curve (AUC). Gradient class-activation-mapping (Grad-CAM) was used to visualize what parts of the images were important for classification. Training/testing data were collected from 262 participants. The top performing network (VGG-16) had top-1 accuracy=56%, top-2 accuracy=78%, recall=.30, precision=.70, and AUC=.85. The network performed better on larger classes (top-1 accuracy: control=62% [n=57], CF=67% [n=85], LAM=69% [n=61]) and outperformed human observers (human top-1 accuracy=40%, network top-1 accuracy=61% on a single training fold). We developed an artificial intelligence tool that could classify disease from xenon ventilation images alone that outperformed human observers. This suggests that xenon images have additional, disease-specific information that could be useful for cases that are clinically challenging or for disease phenotyping.

The global demand for radiologists is increasing rapidly due to a growing reliance on medical imaging services, while the supply of radiologists is not keeping pace. Advances in computer vision and image processing technologies present significant potential to address this gap by enhancing radiologists' capabilities and improving diagnostic accuracy. Large language models (LLMs), particularly generative pre-trained transformers (GPTs), have become the primary approach for understanding and generating textual data. In parallel, vision transformers (ViTs) have proven effective at converting visual data into a format that LLMs can process efficiently. In this paper, we present ChestGPT, a deep-learning framework that integrates the EVA ViT with the Llama 2 LLM to classify diseases and localize regions of interest in chest X-ray images. The ViT converts X-ray images into tokens, which are then fed, together with engineered prompts, into the LLM, enabling joint classification and localization of diseases. This approach incorporates transfer learning techniques to enhance both explainability and performance. The proposed method achieved strong global disease classification performance on the VinDr-CXR dataset, with an F1 score of 0.76, and successfully localized pathologies by generating bounding boxes around the regions of interest. We also outline several task-specific prompts, in addition to general-purpose prompts, for scenarios radiologists might encounter. Overall, this framework offers an assistive tool that can lighten radiologists' workload by providing preliminary findings and regions of interest to facilitate their diagnostic process.

This paper introduces a Content-Based Medical Image Retrieval (CBMIR) system for detecting and retrieving lung disease cases to assist doctors and radiologists in clinical decision-making. The system combines texture-based features using Local Binary Patterns (LBP) with deep learning-based features extracted from pretrained CNN models, including VGG-16, DenseNet121, and InceptionV3. The objective is to identify the optimal fusion of texture and deep features to enhance the image retrieval performance. Various similarity measures, including Euclidean, Manhattan, and cosine similarities, were evaluated, with Cosine Similarity demonstrating the best performance, achieving an average precision of 65.5%. For COVID-19 cases, VGG-16 achieved a precision of 52.5%, while LBP performed best for the normal class with 85% precision. The fusion of LBP, VGG-16, and DenseNet121 excelled in pneumonia cases, with a precision of 93.5%. Overall, VGG-16 delivered the highest average precision of 74.0% across all classes, followed by LBP at 72.0%. The fusion of texture (LBP) and deep features from all CNN models achieved 86% accuracy for the retrieval of the top 10 images, supporting healthcare professionals in making more informed clinical decisions.

Radiology reports are essential in medical imaging, providing critical insights for diagnosis, treatment, and patient management by bridging the gap between radiologists and referring physicians. However, the manual generation of radiology reports is time-consuming and labor-intensive, leading to inefficiencies and delays in clinical workflows, particularly as case volumes increase. Although deep learning approaches have shown promise in automating radiology report generation, existing methods, particularly those based on the encoder-decoder framework, suffer from significant limitations. These include a lack of explainability due to black-box features generated by encoder and limited adaptability to diverse clinical settings. In this study, we address these challenges by proposing a novel deep learning framework for radiology report generation that enhances explainability, accuracy, and adaptability. Our approach replaces traditional black-box features in computer vision with transparent keyword lists, improving the interpretability of the feature extraction process. To generate these keyword lists, we apply a multi-label classification technique, which is further enhanced by an automatic keyword adaptation mechanism. This adaptation dynamically configures the multi-label classification to better adapt specific clinical environments, reducing the reliance on manually curated reference keyword lists and improving model adaptability across diverse datasets. We also introduce a frequency-based multi-label classification strategy to address the issue of keyword imbalance, ensuring that rare but clinically significant terms are accurately identified. Finally, we leverage a pre-trained text-to-text large language model (LLM) to generate human-like, clinically relevant radiology reports from the extracted keyword lists, ensuring linguistic quality and clinical coherence. We evaluate our method using two public datasets, IU-XRay and MIMIC-CXR, demonstrating superior performance over state-of-the-art methods. Our framework not only improves the accuracy and reliability of radiology report generation but also enhances the explainability of the process, fostering greater trust and adoption of AI-driven solutions in clinical practice. Comprehensive ablation studies confirm the robustness and effectiveness of each component, highlighting the significant contributions of our framework to advancing automated radiology reporting. In conclusion, we developed a novel deep-learning based radiology report generation method for preparing high-quality and explainable radiology report for chest X-ray images using the multi-label classification and a text-to-text large language model. Our method could address the lack of explainability in the current workflow and provide a clear and flexible automated pipeline to reduce the workload of radiologists and support the further applications related to Human-AI interactive communications.

Multimodal deep learning holds promise for improving clinical prediction by integrating diverse patient data, including text, imaging, time-series, and structured demographics. Contrastive learning facilitates this integration by producing a unified representation that can be reused across tasks, reducing the need for separate models or encoders. Although contrastive learning has seen success in vision-language domains, its use in clinical settings remains largely limited to image and text pairs. We propose the Pipeline for Contrastive Modality Evaluation and Encoding (PiCME), which systematically assesses five clinical data types from MIMIC: discharge summaries, radiology reports, chest X-rays, demographics, and time-series. We pre-train contrastive models on all 26 combinations of two to five modalities and evaluate their utility on in-hospital mortality and phenotype prediction. To address performance plateaus with more modalities, we introduce a Modality-Gated LSTM that weights each modality according to its contrastively learned importance. Our results show that contrastive models remain competitive with supervised baselines, particularly in three-modality settings. Performance declines beyond three modalities, which supervised models fail to recover. The Modality-Gated LSTM mitigates this drop, improving AUROC from 73.19% to 76.93% and AUPRC from 51.27% to 62.26% in the five-modality setting. We also compare contrastively learned modality importance scores with attribution scores and evaluate generalization across demographic subgroups, highlighting strengths in interpretability and fairness. PiCME is the first to scale contrastive learning across all modality combinations in MIMIC, offering guidance for modality selection, training strategies, and equitable clinical prediction.