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The Berlin definition of ARDS provides standardized diagnostic criteria based on acute onset within one week of a known insult, bilateral chest imaging opacities not explained by other causes, respiratory failure not due to cardiac issues or fluid overload, and impaired oxygenation measured by the PaO₂/FiO₂ ratio, enabling consistent identification in intensive care; however, its clinical use is limited by variability in imaging interpretation and the need for rapid decision-making, often causing delays and inconsistent diagnoses. Current practice relies heavily on subjective assessment of chest X-rays and limited integration of clinical notes and laboratory trends, resulting in moderate inter-observer agreement and reduced diagnostic reliability. To overcome these challenges, a multimodal transformer framework is proposed that integrates chest X-rays, clinical notes, and laboratory data using vision transformers, BERT-based text encoders, and temporally aware lab embeddings, with cross-modal attention enabling interaction across data types and a fusion module producing final ARDS probability estimates. This integrated approach improves diagnostic accuracy by combining complementary information, enhances interpretability through attention mechanisms, and offers a more objective and timely method for ARDS detection, with potential to support earlier intervention and better outcomes in critically ill patients.

Rare diseases identified via chest radiography—such as spontaneous pneumothorax, solitary pulmonary nodules, pleural effusions, and cardiomegaly—occur far less frequently than common conditions like pneumonia or chronic obstructive pulmonary disease. Deep learning models require large, balanced datasets for reliable performance, yet rare pathologies remain underrepresented in clinical repositories, limiting real-world deployability. Conventional augmentation methods (geometric and intensity transformations, elastic deformations) add limited variability without creating new pathological patterns. GAN-based approaches can generate synthetic images but often suffer from mode collapse and unrealistic artifacts that reduce lesion fidelity, restricting their effectiveness for rare disease augmentation. We propose a framework based on denoising diffusion probabilistic models (DDPMs) for conditional synthesis of high-fidelity chest X-ray images. The model supports generation conditioned on class labels, segmentation masks, or text prompts, enabling controlled synthesis of rare pathologies and improving dataset balance. The framework includes a forward diffusion process, a U-Net-based reverse denoising model with attention, a multi-modal conditioning mechanism, a lesion-preserving loss function, and an augmentation pipeline combining real and synthetic data. This allows control over lesion type, location, size, and severity, reducing class imbalance and improving classifier performance on rare diseases, as validated through AUC improvements and radiologist assessment. Overall, diffusion-based models provide a scalable and clinically relevant solution for rare disease augmentation in chest radiography, overcoming key limitations of traditional and GAN-based methods and enabling effective use of datasets such as CheXpert, MIMIC-CXR, and ChestX-ray14.

Chest X-ray report generation is time-consuming and contributes to radiologist workload and burnout, motivating the need for AI systems that can reduce cognitive burden while preserving clinical accuracy. Although encoder-decoder models can generate reports from images, they often suffer from hallucinations, producing findings that are not present or missing real abnormalities due to lack of explicit grounding in evidence, making them unreliable for clinical use. To address this, we propose a cross-modal retrieval framework that generates reports by retrieving and assembling clinically validated sentences from existing radiology reports rather than generating text from scratch. The system uses contrastive learning to align chest X-ray image patches with report sentences in a shared embedding space, enabling retrieval of the most relevant clinical descriptions. A patch encoder extracts visual features, a sentence encoder represents report text, and a retrieval module identifies semantically matching sentences, which are then composed into a coherent final report. Because all outputs are sourced from real clinical reports, the method substantially reduces hallucinations while improving factual reliability and interpretability. This retrieval-based approach offers a scalable and safer alternative to generative models and can be evaluated on datasets such as MIMIC-CXR and CheXpert for clinical accuracy and retrieval performance.

Chest X-ray is a commonly used imaging tool in both acute and routine care, but the increasing reporting workload highlights the need for structured preliminary reports that aid triage, reduce delays, and ensure clinical relevance. Current AI systems often focus on classification or generic report generation, neglecting critical factors like free-text radiology requests, clinical history, and comparison context, leading to reports that, while technically fluent, are insufficiently focused. This article proposes a multimodal vision-language model that interprets both chest X-ray images and free-text radiology requests to generate structured preliminary reports directly addressing the clinical question. The model combines a radiographic encoder based on vision transformers, a text encoder for requests and prior reports, a cross-modal attention module, and a structured report decoder, organizing the output into relevant sections such as indication, technique, findings, impression, comparison, and answer-to-request. By aligning report generation with the clinical request, the model ensures that it answers specific questions—such as concerns about pneumonia, pulmonary oedema, or pneumothorax—improving report relevance, reducing misinterpretation, and supporting safer human-in-the-loop review. However, its effectiveness relies on accurate alignment, factual consistency, uncertainty management, and validation in real-world radiology settings.