Recently, large language models (LLMs) have driven promis ing progress in lossless image compression. However, di rectly adopting existing paradigms for medical images suf fers from an unsatisfactory trade-off between compression performance and efficiency. Moreover, existing LLM-based compressors often overlook the security of the compres sion process, which is critical in modern medical scenarios. To this end, we propose a novel joint lossless compression and steganography framework. Inspired by bit plane slicing (BPS), we find it feasible to securely embed privacy messages into medical images in an invisible manner. Based on this in sight, an adaptive modalities decomposition strategy is first devised to partition the entire image into two segments, pro viding global and local modalities for subsequent dual-path lossless compression. During this dual-path stage, we inno vatively propose a segmented message steganography algo rithm within the local modality path to ensure the security of the compression process. Coupled with the proposed anatom ical priors-based low-rank adaptation (A-LoRA) fine-tuning strategy, extensive experimental results demonstrate the su periority of our proposed method in terms of compression ra tios, efficiency, and security. The source code will be made publicly available.

Fine needle aspiration is a well-known procedure for the diagnosis and management of solid lesions. The approach to cystic lesions on fine needle-aspiration is becoming a popular diagnostic tool due to the increased availability of high-quality cross-sectional imaging such as computed tomography and ultrasound guided procedures like endoscopic ultrasound. Cystic lesions are closed cavities containing liquid, sometimes partially solid with various internal neoplastic and non-neoplastic components. The most frequently punctured cysts are in the neck (thyroid and salivary glands), mediastinum, breast and abdomen (pancreas and liver). The diagnostic accuracy of cytological cyst sampling is highly dependent on laboratory material management. This review highlights how to approach the main features of superficial and deep organ cysts using basic cytological techniques (direct smears, cytocentrifugation), liquid-based cytology and cell block. We show the role of a multimodal approach that can lead to a wider implementation of ancillary tests (biochemical, immunocytochemical and molecular) to improve diagnostic accuracy and clinical management of patients with cystic lesions. In the near future, artificial intelligence models will offer detection, classification and prediction capabilities for various cystic lesions. Two examples in pancreatic and thyroid cytopathology are particularly developed.

BackgroundMyocarditis is a life-threatening disease with significant hemodynamic risks during the acute phase. Although histopathological examination of myocardial biopsy specimens remains the gold standard for diagnosis, there is no established method for objectively quantifying cardiomyocyte damage. We aimed to develop an AI model to evaluate clinical myocarditis severity using comprehensive pathology data. MethodsWe retrospectively analyzed 314 patients (1076 samples) who underwent myocardial biopsy from 2002 to 2021 at the National Cerebrovascular Center. Among these patients, 158 were diagnosed with myocarditis based on the Dallas criteria. A Multiple Instance Learning (MIL) model served as a pre-trained classifier to detect myocarditis across whole-slide images. We then constructed two clinical severity-prediction models: (1) a logistic regression model (Model 1) using the density of inflammatory cells per unit area, and (2) a Transformer-based model (Model 2), which processed the top-ranked patches identified by the MIL model to predict clinical severe outcomes. ResultsModel 1 achieved an AUROC of 0.809, indicating a robust association between inflammatory cell density and severe myocarditis. In contrast, Model 2, the Transformer-based approach, yielded an AUROC of 0.993 and demonstrated higher accuracy and precision for severity prediction. Attention score visualizations showed that Model 2 captured both inflammatory cell infiltration and additional morphological features. These findings suggest that combining MIL with Transformer architectures enables more comprehensive identification of key histological markers associated with clinical severe disease. ConclusionsOur results highlight that a Transformer-based AI model analyzing whole-slide pathology images can accurately assess clinical myocarditis severity. Moreover, simply quantifying the extent of inflammatory cell infiltration also correlates strongly with clinical outcomes. These methods offer a promising avenue for improving diagnostic precision, guiding treatment decisions, and ultimately enhancing patient management. Future prospective studies are warranted to validate these models in broader clinical settings and facilitate their integration into routine pathological workflows. What is new?- This is the first study to apply an AI model for the diagnosis and severity assessment of myocarditis. - New evidence shows that inflammatory cell infiltration is related to the severity of myocarditis. - Using information from the entire tissue, not just inflammatory cells, allows for a more accurate assessment of myocarditis severity. What are the clinical implications?- The use of the AI model allows for an unprecedented histological evaluation of myocarditis severity, which can enhance early diagnosis and intervention strategies. - Rapid and precise assessments of myocarditis severity by the AI model can support clinicians in making timely and appropriate treatment decisions, potentially improving patient outcomes. - The incorporation of this AI model into clinical practice may streamline diagnostic workflows and optimize the allocation of medical resources, enhancing overall patient care.

This review provides a comprehensive overview of current practices and recent advancements in the diagnosis and treatment of renal cancer. It introduces updates in histological classification and explains the imaging characteristics of each tumour based on these changes. The review highlights state-of-the-art imaging modalities, including magnetic resonance imaging, computed tomography, positron emission tomography, and ultrasound, emphasising their crucial role in tumour characterisation and optimising treatment planning. Emerging technologies, such as radiomics and artificial intelligence, are also discussed for their transformative impact on enhancing diagnostic precision, prognostic prediction, and personalised patient management. Furthermore, the review explores current treatment options, including minimally invasive techniques such as cryoablation, radiofrequency ablation, and stereotactic body radiation therapy, as well as systemic therapies such as immune checkpoint inhibitors and targeted therapies.

Clinical decision-making relies heavily on understanding relative positions of anatomical structures and anomalies. Therefore, for Vision-Language Models (VLMs) to be applicable in clinical practice, the ability to accurately determine relative positions on medical images is a fundamental prerequisite. Despite its importance, this capability remains highly underexplored. To address this gap, we evaluate the ability of state-of-the-art VLMs, GPT-4o, Llama3.2, Pixtral, and JanusPro, and find that all models fail at this fundamental task. Inspired by successful approaches in computer vision, we investigate whether visual prompts, such as alphanumeric or colored markers placed on anatomical structures, can enhance performance. While these markers provide moderate improvements, results remain significantly lower on medical images compared to observations made on natural images. Our evaluations suggest that, in medical imaging, VLMs rely more on prior anatomical knowledge than on actual image content for answering relative position questions, often leading to incorrect conclusions. To facilitate further research in this area, we introduce the MIRP , Medical Imaging Relative Positioning, benchmark dataset, designed to systematically evaluate the capability to identify relative positions in medical images.

Interoperability (the ability of data or tools from non-cooperating resources to integrate or work together with minimal effort) is particularly important for curation of multimodal datasets from multiple data sources. The Medical Imaging and Data Resource Center (MIDRC), a multi-institutional collaborative initiative to collect, curate, and share medical imaging datasets, has made interoperability with other data commons one of its top priorities. The purpose of this study was to demonstrate the interoperability between MIDRC and two other data repositories, BioData Catalyst (BDC) and National Clinical Cohort Collaborative (N3C). Using interoperability capabilities of the data repositories, we built two cohorts for example use cases, with each containing clinical and imaging data on matched patients. The representativeness of the cohorts is characterized by comparing with CDC population statistics using the Jensen-Shannon distance. The process and methods of interoperability demonstrated in this work can be utilized by MIDRC, BDC, and N3C users to create multimodal datasets for development of artificial intelligence/machine learning models.

Alzheimer's disease (AD) is accompanied by alterations in retinal vascular density (VD), but the mechanisms remain unclear. This study investigated the relationship among cerebral amyloid-β (Aβ) deposition, VD, and cognitive decline. We enrolled 92 participants, including 47 AD patients and 45 healthy control (HC) participants. VD across retinal subregions was quantified using deep learning-based fundus photography, and cerebral Aβ deposition was measured with ¹⁸F-florbetapir (¹⁸F-AV45) PET/MRI. Using the minimum bounding circle of the optic disc as the diameter (papilla-diameter, PD), VD (total, 0.5-1.0 PD, 1.0-1.5 PD, 1.5-2.0 PD, 2.0-2.5 PD) was calculated. Standardized uptake value ratio (SUVR) for Aβ deposition was computed for global and regional cortical areas, using the cerebellar cortex as the reference region. Cognitive performance was assessed with the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Pearson correlation, multiple linear regression, and mediation analyses were used to explore Aβ deposition, VD, and cognition. AD patients exhibited significantly lower VD in all subregions compared to HC (p < 0.05). Reduced VD correlated with higher SUVR in the global cortex and a decline in cognitive abilities (p < 0.05). Mediation analysis indicated that VD influenced MMSE and MoCA through SUVR in the global cortex, with the most pronounced effects observed in the 1.0-1.5 PD range. Retinal VD is associated with cognitive decline, a relationship primarily mediated by cerebral Aβ deposition measured via ¹⁸F-AV45 PET. These findings highlight the potential of retinal VD as a biomarker for early detection in AD.

In clinical practice, medical image analysis often requires efficient execution on resource-constrained mobile devices. However, existing mobile models-primarily optimized for natural images-tend to perform poorly on medical tasks due to the significant information density gap between natural and medical domains. Combining computational efficiency with medical imaging-specific architectural advantages remains a challenge when developing lightweight, universal, and high-performing networks. To address this, we propose a mobile model called Mobile U-shaped Vision Transformer (Mobile U-ViT) tailored for medical image segmentation. Specifically, we employ the newly purposed ConvUtr as a hierarchical patch embedding, featuring a parameter-efficient large-kernel CNN with inverted bottleneck fusion. This design exhibits transformer-like representation learning capacity while being lighter and faster. To enable efficient local-global information exchange, we introduce a novel Large-kernel Local-Global-Local (LGL) block that effectively balances the low information density and high-level semantic discrepancy of medical images. Finally, we incorporate a shallow and lightweight transformer bottleneck for long-range modeling and employ a cascaded decoder with downsample skip connections for dense prediction. Despite its reduced computational demands, our medical-optimized architecture achieves state-of-the-art performance across eight public 2D and 3D datasets covering diverse imaging modalities, including zero-shot testing on four unseen datasets. These results establish it as an efficient yet powerful and generalization solution for mobile medical image analysis. Code is available at https://github.com/FengheTan9/Mobile-U-ViT.

Recent advancements in large language models (LLMs) have led to multimodal LLMs (MLLMs), which integrate multiple data modalities beyond text. Although MLLMs show promise, there is a gap in the literature that empirically demonstrates their impact in healthcare. This paper summarizes the applications of MLLMs in healthcare, highlighting their potential to transform health practices. A rapid literature review was conducted in August 2024 using World Health Organization (WHO) rapid-review methodology and PRISMA standards, with searches across four databases (Scopus, Medline, PubMed and ACM Digital Library) and top-tier conferences-including NeurIPS, ICML, AAAI, MICCAI, CVPR, ACL and EMNLP. Articles on MLLMs healthcare applications were included for analysis based on inclusion and exclusion criteria. The search yielded 115 articles, 39 included in the final analysis. Of these, 77% appeared online (preprints and published) in 2024, reflecting the emergence of MLLMs. 80% of studies were from Asia and North America (mainly China and US), with Europe lagging. Studies split evenly between pre-built MLLMs evaluations (60% focused on GPT versions) and custom MLLMs/frameworks development with task-specific customizations. About 81% of studies examined MLLMs for diagnosis and reporting in radiology, pathology, and ophthalmology, with additional applications in education, surgery, and mental health. Prompting strategies, used in 80% of studies, improved performance in nearly half. However, evaluation practices were inconsistent with 67% reported accuracy. Error analysis was mostly anecdotal, with only 18% categorized failure types. Only 13% validated explainability through clinician feedback. Clinical deployment was demonstrated in just 3% of studies, and workflow integration, governance, and safety were rarely addressed. MLLMs offer substantial potential for healthcare transformation through multimodal data integration. Yet, methodological inconsistencies, limited validation, and underdeveloped deployment strategies highlight the need for standardized evaluation metrics, structured error analysis, and human-centered design to support safe, scalable, and trustworthy clinical adoption.

Liver diseases pose a significant global health challenge due to their silent progression and high mortality. Proper interpretation of radiology reports is essential for the evaluation and management of these conditions but is limited by variability in reporting styles and the complexity of unstructured medical language. In this context, Natural Language Processing (NLP) techniques and Large Language Models (LLMs) have emerged as promising tools to extract relevant clinical information from unstructured liver radiology reports. This work reviews, from a practical point of view, the current state of NLP and LLM applications for liver disease classification, clinical feature extraction, diagnostic support, and staging from reports. It also discusses existing limitations, such as the need for high-quality annotated data, lack of explainability, and challenges in clinical integration. With responsible and validated implementation, these technologies have the potential to transform liver clinical management by enabling faster and more accurate diagnoses and optimizing radiology workflows, ultimately improving patient care in liver diseases.