Recent advances in Artificial Intelligence (AI) have started disrupting the healthcare industry, especially medical imaging, and AI devices are increasingly being deployed into clinical practice. Such classifiers have previously demonstrated the ability to discern a range of protected demographic attributes (like race, age, sex) from medical images with unexpectedly high performance, a sensitive task which is difficult even for trained physicians. In this study, we motivate and introduce a general explainable AI (XAI) framework called DREAM (DiscoveRing and Explaining AI Mechanisms) for interpreting how AI models trained on medical images predict protected attributes. Focusing on two modalities, radiology and dermatology, we are successfully able to train high-performing classifiers for predicting race from chest x-rays (ROC-AUC score of [~]0.96) and sex from dermoscopic lesions (ROC-AUC score of [~]0.78). We highlight how incorrect use of these demographic shortcuts can have a detrimental effect on the performance of a clinically relevant downstream task like disease diagnosis under a domain shift. Further, we employ various XAI techniques to identify specific signals which can be leveraged to predict sex. Finally, we propose a technique, which we callremoval via balancing, to quantify how much a signal contributes to the classification performance. Using this technique and the signals identified, we are able to explain [~]15% of the total performance for radiology and [~]42% of the total performance for dermatology. We envision DREAM to be broadly applicable to other modalities and demographic attributes. This analysis not only underscores the importance of cautious AI application in healthcare but also opens avenues for improving the transparency and reliability of AI-driven diagnostic tools.

In intensive care units (ICUs), patients with complex clinical conditions require vigilant monitoring and prompt interventions. Chest X-rays (CXRs) are a vital diagnostic tool, providing insights into clinical trajectories, but their irregular acquisition limits their utility. Existing tools for CXR interpretation are constrained by cross-sectional analysis, failing to capture temporal dynamics. To address this, we introduce CXR-TFT, a novel multi-modal framework that integrates temporally sparse CXR imaging and radiology reports with high-frequency clinical data, such as vital signs, laboratory values, and respiratory flow sheets, to predict the trajectory of CXR findings in critically ill patients. CXR-TFT leverages latent embeddings from a vision encoder that are temporally aligned with hourly clinical data through interpolation. A transformer model is then trained to predict CXR embeddings at each hour, conditioned on previous embeddings and clinical measurements. In a retrospective study of 20,000 ICU patients, CXR-TFT demonstrated high accuracy in forecasting abnormal CXR findings up to 12 hours before they became radiographically evident. This predictive capability in clinical data holds significant potential for enhancing the management of time-sensitive conditions like acute respiratory distress syndrome, where early intervention is crucial and diagnoses are often delayed. By providing distinctive temporal resolution in prognostic CXR analysis, CXR-TFT offers actionable 'whole patient' insights that can directly improve clinical outcomes.

Scoliosis affects roughly 2 to 4 percent of adolescents, and treatment decisions depend on precise Cobb angle measurement. Manual assessment is time consuming and subject to inter observer variation. We conducted a retrospective, multi centre evaluation of a fully automated deep learning software (Carebot AI Bones, Spine Measurement functionality; Carebot s.r.o.) on 103 standing anteroposterior whole spine radiographs collected from ten hospitals. Two musculoskeletal radiologists independently measured each study and served as reference readers. Agreement between the AI and each radiologist was assessed with Bland Altman analysis, mean absolute error (MAE), root mean squared error (RMSE), Pearson correlation coefficient, and Cohen kappa for four grade severity classification. Against Radiologist 1 the AI achieved an MAE of 3.89 degrees (RMSE 4.77 degrees) with a bias of 0.70 degrees and limits of agreement from minus 8.59 to plus 9.99 degrees. Against Radiologist 2 the AI achieved an MAE of 3.90 degrees (RMSE 5.68 degrees) with a bias of 2.14 degrees and limits from minus 8.23 to plus 12.50 degrees. Pearson correlations were r equals 0.906 and r equals 0.880 (inter reader r equals 0.928), while Cohen kappa for severity grading reached 0.51 and 0.64 (inter reader kappa 0.59). These results demonstrate that the proposed software reproduces expert level Cobb angle measurements and categorical grading across multiple centres, suggesting its utility for streamlining scoliosis reporting and triage in clinical workflows.

Chest X-rays (CXR) rank among the most conducted X-ray examinations. They often require repeat imaging due to inadequate quality, leading to increased radiation exposure and delays in patient care and diagnosis. This research assesses the efficacy of DenseNet121 and YOLOv8 neural networks in detecting suboptimal CXRs, which may minimise delays and enhance patient outcomes. The study included 3587 patients with a median age of 67 (0-102). It utilised an initial dataset comprising 10,000 CXRs randomly divided into a training subset (4000 optimal and 4000 suboptimal) and a validation subset (400 optimal and 400 suboptimal). The test subset (25 optimal and 25 suboptimal) was curated from the remaining images to provide adequate variation. Neural networks DenseNet121 and YOLOv8 were chosen due to their capabilities in image classification. DenseNet121 is a robust, well-tested model in the medical industry with high accuracy in object recognition. YOLOv8 is a cutting-edge commercial model targeted at all industries. Their performance was assessed via the area under the receiver operating curve (AUROC) and compared to radiologist classification, utilising the chi-squared test. DenseNet121 attained an AUROC of 0.97, while YOLOv8 recorded a score of 0.95, indicating a strong capability in differentiating between optimal and suboptimal CXRs. The alignment between radiologists and models exhibited variability, partly due to the lack of clinical indications. However, the performance was not statistically significant. Both AI models effectively classified chest X-ray quality, demonstrating the potential for providing radiographers with feedback to improve image quality. Notably, this was the first study to include both PA and lateral CXRs as well as paediatric cases and the first to evaluate YOLOv8 for this application.

Lung neuroendocrine tumours (NETs, also known as carcinoids) are rapidly rising in incidence worldwide but have unknown aetiology and limited therapeutic options beyond surgery. We conducted multi-omic analyses on over 300 lung NETs including whole-genome sequencing (WGS), transcriptome profiling, methylation arrays, spatial RNA sequencing, and spatial proteomics. The integration of multi-omic data provides definitive proof of the existence of four strikingly different molecular groups that vary in patient characteristics, genomic and transcriptomic profiles, microenvironment, and morphology, as much as distinct diseases. Among these, we identify a new molecular group, enriched for highly aggressive supra-carcinoids, that displays an immune-rich microenvironment linked to tumour--macrophage crosstalk, and we uncover an undifferentiated cell population within supra-carcinoids, explaining their molecular and behavioural link to high-grade lung neuroendocrine carcinomas. Deep learning models accurately identified the Ca A1, Ca A2, and Ca B groups based on morphology alone, outperforming current histological criteria. The characteristic tumour microenvironment of supra-carcinoids and the validation of a panel of immunohistochemistry markers for the other three molecular groups demonstrates that these groups can be accurately identified based solely on morphological features, facilitating their implementation in the clinical setting. Our proposed morpho-molecular classification highlights group-specific therapeutic opportunities, including DLL3, FGFR, TERT, and BRAF inhibitors. Overall, our findings unify previously proposed molecular classifications and refine the lung cancer map by revealing novel tumour types and potential treatments, with significant implications for prognosis and treatment decision-making.

BackgroundFifth metatarsal (5MT) fractures are common but challenging to diagnose, particularly with limited expertise or subtle fractures. Deep learning shows promise but faces limitations due to image quality requirements. This study develops a deep learning model to detect 5MT fractures from smartphone-captured radiograph images, enhancing accessibility of diagnostic tools. MethodsA retrospective study included patients aged >18 with 5MT fractures (n=1240) and controls (n=1224). Radiographs (AP, oblique, lateral) from Electronic Health Records (EHR) were obtained and photographed using a smartphone, creating a new dataset (SP). Models using ResNet 152V2 were trained on EHR, SP, and combined datasets, then evaluated on a separate smartphone test dataset (SP-test). ResultsOn validation, the SP model achieved optimal performance (AUROC: 0.99). On the SP-test dataset, the EHR models performance decreased (AUROC: 0.83), whereas SP and combined models maintained high performance (AUROC: 0.99). ConclusionsSmartphone-specific deep learning models effectively detect 5MT fractures, suggesting their practical utility in resource-limited settings.

Background: Shoulder fractures are often underdiagnosed, especially in emergency and high-volume clinical settings. Studies report up to 10% of such fractures may be missed by radiologists. AI-driven tools offer a scalable way to assist early detection and reduce diagnostic delays. We address this gap through a dedicated AI system for shoulder radiographs. Methods: We developed a multi-model deep learning system using 10,000 annotated shoulder X-rays. Architectures include Faster R-CNN (ResNet50-FPN, ResNeXt), EfficientDet, and RF-DETR. To enhance detection, we applied bounding box and classification-level ensemble techniques such as Soft-NMS, WBF, and NMW fusion. Results: The NMW ensemble achieved 95.5% accuracy and an F1-score of 0.9610, outperforming individual models across all key metrics. It demonstrated strong recall and localization precision, confirming its effectiveness for clinical fracture detection in shoulder X-rays. Conclusion: The results show ensemble-based AI can reliably detect shoulder fractures in radiographs with high clinical relevance. The model's accuracy and deployment readiness position it well for integration into real-time diagnostic workflows. The current model is limited to binary fracture detection, reflecting its design for rapid screening and triage support rather than detailed orthopedic classification.

This study aimed to evaluate the diagnostic performance of ChatGPT-4o, a large language model developed by OpenAI, in challenging cases of oral and maxillofacial diseases presented in the <i>Clinicopathologic Conference</i> section of the journal <i>Oral Surgery</i>, <i>Oral Medicine</i>, <i>Oral Pathology</i>, <i>Oral Radiology</i>. A total of 123 diagnostically challenging oral and maxillofacial cases published in the aforementioned journal were retrospectively collected. The case presentations, which included detailed clinical, radiographic, and sometimes histopathologic descriptions, were input into ChatGPT-4o. The model was prompted to provide a single most likely diagnosis for each case. These outputs were then compared to the final diagnoses established by expert consensus in each original case report. The accuracy of ChatGPT-4o was calculated based on exact diagnostic matches. ChatGPT-4o correctly diagnosed 96 out of 123 cases, achieving an overall diagnostic accuracy of 78%. Nevertheless, even in cases where the exact diagnosis was not provided, the model often suggested one of the clinically reasonable differential diagnoses. ChatGPT-4o demonstrates a promising ability to assist in the diagnostic process of complex maxillofacial conditions, with a relatively high accuracy rate in challenging cases. While it is not a replacement for expert clinical judgment, large language models may offer valuable decision support in oral and maxillofacial radiology, particularly in educational or consultative contexts. Not applicable.

In high energy density physics (HEDP) and inertial confinement fusion (ICF), predictive modeling is complicated by uncertainty in parameters that characterize various aspects of the modeled system, such as those characterizing material properties, equation of state (EOS), opacities, and initial conditions. Typically, however, these parameters are not directly observable. What is observed instead is a time sequence of radiographic projections using X-rays. In this work, we define a set of sparse hydrodynamic features derived from the outgoing shock profile and outer material edge, which can be obtained from radiographic measurements, to directly infer such parameters. Our machine learning (ML)-based methodology involves a pipeline of two architectures, a radiograph-to-features network (R2FNet) and a features-to-parameters network (F2PNet), that are trained independently and later combined to approximate a posterior distribution for the parameters from radiographs. We show that the machine learning architectures are able to accurately infer initial conditions and EOS parameters, and that the estimated parameters can be used in a hydrodynamics code to obtain density fields, shocks, and material interfaces that satisfy thermodynamic and hydrodynamic consistency. Finally, we demonstrate that features resulting from an unknown EOS model can be successfully mapped onto parameters of a chosen analytical EOS model, implying that network predictions are learning physics, with a degree of invariance to the underlying choice of EOS model. To the best of our knowledge, our framework is the first demonstration of recovering both thermodynamic and hydrodynamic consistent density fields from noisy radiographs.

Medical image synthesis presents unique challenges due to the inherent complexity and high-resolution details required in clinical contexts. Traditional generative architectures such as Generative Adversarial Networks (GANs) or Variational Auto Encoder (VAEs) have shown great promise for high-resolution image generation but struggle with preserving fine-grained details that are key for accurate diagnosis. To address this issue, we introduce Pixel Perfect MegaMed, the first vision-language foundation model to synthesize images at resolutions of 1024x1024. Our method deploys a multi-scale transformer architecture designed specifically for ultra-high resolution medical image generation, enabling the preservation of both global anatomical context and local image-level details. By leveraging vision-language alignment techniques tailored to medical terminology and imaging modalities, Pixel Perfect MegaMed bridges the gap between textual descriptions and visual representations at unprecedented resolution levels. We apply our model to the CheXpert dataset and demonstrate its ability to generate clinically faithful chest X-rays from text prompts. Beyond visual quality, these high-resolution synthetic images prove valuable for downstream tasks such as classification, showing measurable performance gains when used for data augmentation, particularly in low-data regimes. Our code is accessible through the project website - https://tehraninasab.github.io/pixelperfect-megamed.