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.

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.

Osteoporosis (OP), a systemic skeletal disease characterized by compromised bone strength and elevated fracture susceptibility, represents a growing global health challenge that necessitates early detection and accurate risk stratification. With the exponential growth of multidimensional biomedical data in OP research, feature selection has become an indispensable machine learning paradigm that improves model generalizability. At the same time, it preserves clinical interpretability and enhances predictive accuracy. This perspective article systematically reviews the transformative role of feature selection methodologies across three critical domains of OP investigation: 1) multi-omics biomarker identification, 2) diagnostic pattern recognition, and 3) fracture risk prognostication. In biomarker discovery, advanced feature selection algorithms systematically refine high-dimensional multi-omics datasets (genomic, proteomic, metabolomic) to isolate key molecular signatures correlated with bone mineral density (BMD) trajectories and microarchitectural deterioration. For clinical diagnostics, these techniques enable efficient extraction of discriminative pattern from multimodal imaging data, including dual-energy X-ray absorptiometry (DXA), quantitative computed tomography (CT), and emerging dental radiographic biomarkers. In prognostic modeling, strategic variable selection optimizes prognostic accuracy by integrating demographic, biochemical, and biomechanical predictors while migrating overfitting in heterogeneous patient cohorts. Current challenges include heterogeneity in dataset quality and dimensionality, translational gaps between algorithmic outputs and clinical decision parameters, and limited reproducibility across diverse populations. Future directions should prioritize the development of adaptive feature selection frameworks capable of dynamic multi-omics data integration, coupled with hybrid intelligence systems that synergize machine-derived biomarkers with clinician expertise. Addressing these challenges requires coordinated interdisciplinary efforts to establish standardized validation protocols and create clinician-friendly decision support interfaces, ultimately bridging the gap between computational OP research and personalized patient care.

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.

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.

To explore the potential role of generative artificial intelligence (GenAI) in enhancing patient counseling following prenatal ultrasound diagnosis of fetal malformations, with an emphasis on clinical utility, patient comprehension, and ethical implementation. The detection of fetal anomalies during the mid-trimester ultrasound is emotionally distressing for patients and presents significant challenges in communication and decision-making. Generative AI tools, such as GPT-4 and similar models, offer novel opportunities to support clinicians in delivering accurate, empathetic, and accessible counseling while preserving the physician's central role. We present a narrative review and applied framework illustrating how GenAI can assist obstetricians before, during, and after the fetal anomaly scan. Use cases include lay summaries, visual aids, anticipatory guidance, multilingual translation, and emotional support. Tables and sample prompts demonstrate practical applications across a range of anomalies.

Sports injuries are a significant concern for athletes at all levels of competition, ranging from acute traumas to chronic conditions. Prompt diagnosis and effective treatment are crucial for an athlete's recovery and quality of life. Traditionally, sports injury diagnosis has relied on clinical assessments, patient history, and basic imaging techniques such as X-rays, ultrasound, and magnetic resonance imaging (MRI). However, recent technological advancements have revolutionized the field of sports medicine, offering more accurate diagnoses and targeted treatment strategies. High-resolution MRI and CT scans provide detailed images of deep tissue injuries, while advanced ultrasound technology enables on-field diagnostics. Wearable sensor devices and machine learning algorithms allow real-time monitoring of an athlete's movements and physical loads, facilitating early intervention and injury risk prediction. Regenerative medicine, including stem cell therapy and tissue engineering, has emerged as a transformative approach to healing damaged tissues and reducing treatment time. Despite the challenges of high costs, lack of skilled personnel, and ethical considerations, the integration of artificial intelligence and machine learning into sports medicine holds immense potential for revolutionizing injury prevention and management. As these advancements continue to evolve, they are expected to extend athletes' careers and enhance their overall quality of life. This review summarizes conventional methods to diagnose and manage injuries and provides insights into the recent advancements in the field of sports science and medicine. It also states future outlook on the diagnosis and treatment of sports injuries.

Substance use disorders (SUD) are chronic, relapsing conditions marked by high variability in treatment response and frequent relapse. This variability arises from complex interactions among behavioral, environmental, and biological factors unique to each individual. Precision medicine, which tailors treatment to patient-specific characteristics, offers a promising avenue to address these challenges. This review explores key factors influencing SUD, including severity, comorbidities, drug use motives, polysubstance use, cognitive impairments, and biological and environmental influences. Advanced neuroimaging, such as MRI and PET, enables patient subtyping by identifying altered brain mechanisms, including reward, relief, and cognitive pathways, and striatal dopamine D_2/3 receptor binding. Pharmacogenetic and epigenetic studies uncover how variations in dopaminergic, serotoninergic, and opioidergic systems shape treatment outcomes. Emerging biomarkers, such as neurofilament light chain, offer non-invasive relapse monitoring. Multifactorial models integrating behavioral and neural markers outperform single-factor approaches in predicting treatment success. Machine learning refines these models, while longitudinal and preclinical studies support individualized care. Despite translational hurdles, precision medicine offers transformative potential for improving SUD treatment outcomes.

Diffuse midline glioma (DMG) is a rare, aggressive, and fatal tumor that largely occurs in the pediatric population. To improve outcomes, it is important to characterize DMGs, which can be performed via magnetic resonance imaging (MRI) assessment. Recently, artificial intelligence (AI) and advanced imaging have demonstrated their potential to improve the evaluation of various brain tumors, gleaning more information from imaging data than is possible without these methods. This narrative review compiles the existing literature on the intersection of MRI-based AI use and DMG tumors. The applications of AI in DMG revolve around classification and diagnosis, segmentation, radiogenomics, and prognosis/survival prediction. Currently published articles have utilized a wide spectrum of AI algorithms, from traditional machine learning and radiomics to neural networks. Challenges include the lack of cohorts of DMG patients with publicly available, multi-institutional, multimodal imaging and genomics datasets as well as the overall rarity of the disease. As an adjunct to AI, advanced MRI techniques, including diffusion-weighted imaging, perfusion-weighted imaging, and Magnetic Resonance Spectroscopy (MRS), as well as positron emission tomography (PET), provide additional insights into DMGs. Establishing AI models in conjunction with advanced imaging modalities has the potential to push clinical practice toward precision medicine.

Feature matching is a cornerstone task in computer vision, essential for applications such as image retrieval, stereo matching, 3D reconstruction, and SLAM. This survey comprehensively reviews modality-based feature matching, exploring traditional handcrafted methods and emphasizing contemporary deep learning approaches across various modalities, including RGB images, depth images, 3D point clouds, LiDAR scans, medical images, and vision-language interactions. Traditional methods, leveraging detectors like Harris corners and descriptors such as SIFT and ORB, demonstrate robustness under moderate intra-modality variations but struggle with significant modality gaps. Contemporary deep learning-based methods, exemplified by detector-free strategies like CNN-based SuperPoint and transformer-based LoFTR, substantially improve robustness and adaptability across modalities. We highlight modality-aware advancements, such as geometric and depth-specific descriptors for depth images, sparse and dense learning methods for 3D point clouds, attention-enhanced neural networks for LiDAR scans, and specialized solutions like the MIND descriptor for complex medical image matching. Cross-modal applications, particularly in medical image registration and vision-language tasks, underscore the evolution of feature matching to handle increasingly diverse data interactions.