The exponential growth of medical imaging has created significant challenges in data storage, transmission, and management for healthcare systems. In this vein, efficient compression becomes increasingly important. Unlike natural image compression, medical image compression prioritizes preserving diagnostic details and structural integrity, imposing stricter quality requirements and demanding fast, memory-efficient algorithms that balance computational complexity with clinically acceptable reconstruction quality. Meanwhile, the medical imaging family includes a plethora of modalities, each possessing different requirements. For example, 2D medical image (e.g., X-rays, histopathological images) compression focuses on exploiting intra-slice spatial redundancy, while volumetric medical image faces require handling intra-slice and inter-slice spatial correlations, and 4D dynamic imaging (e.g., time-series CT/MRI, 4D ultrasound) additionally demands processing temporal correlations between consecutive time frames. Traditional compression methods, grounded in mathematical transforms and information theory principles, provide solid theoretical foundations, predictable performance, and high standardization levels, with extensive validation in clinical environments. In contrast, deep learning-based approaches demonstrate remarkable adaptive learning capabilities and can capture complex statistical characteristics and semantic information within medical images. This comprehensive survey establishes a two-facet taxonomy based on data structure (2D vs 3D/4D) and technical approaches (traditional vs learning-based), thereby systematically presenting the complete technological evolution, analyzing the unique technical challenges, and prospecting future directions in medical image compression.

The Segment Anything Model (SAM) has revolutionized image segmentation through its innovative prompt-based approach, yet the critical role of prompt engineering in its success remains underexplored. This paper presents the first comprehensive survey focusing specifically on prompt engineering techniques for SAM and its variants. We systematically organize and analyze the rapidly growing body of work in this emerging field, covering fundamental methodologies, practical applications, and key challenges. Our review reveals how prompt engineering has evolved from simple geometric inputs to sophisticated multimodal approaches, enabling SAM's adaptation across diverse domains including medical imaging and remote sensing. We identify unique challenges in prompt optimization and discuss promising research directions. This survey fills an important gap in the literature by providing a structured framework for understanding and advancing prompt engineering in foundation models for segmentation.

Radiology education is challenged by increasing clinical workloads, limiting trainee supervision time and hindering real-time feedback. Large language models (LLMs) can enhance radiology education by providing real-time guidance, feedback, and educational resources while supporting efficient clinical workflows. We present an interpretation-centric framework for integrating LLMs into radiology education subdivided into distinct phases spanning pre-dictation preparation, active dictation support, and post-dictation analysis. In the pre-dictation phase, LLMs can analyze clinical data and provide context-aware summaries of each case, suggest relevant educational resources, and triage cases based on their educational value. In the active dictation phase, LLMs can provide real-time educational support through processes such as differential diagnosis support, completeness guidance, classification schema assistance, structured follow-up guidance, and embedded educational resources. In the post-dictation phase, LLMs can be used to analyze discrepancies between trainee and attending reports, identify areas for improvement, provide targeted educational recommendations, track trainee performance over time, and analyze the radiologic entities that trainees encounter. This framework offers a comprehensive approach to integrating LLMs into radiology education, with the potential to enhance trainee learning while preserving clinical efficiency.

Neurological Disorders (ND) affect a large portion of the global population, impacting the brain, spinal cord, and nerves. These disorders fall into categories such as NeuroDevelopmental (NDD), NeuroBiological (NBD), and NeuroDegenerative (ND_e) disorders, which range from common to rare conditions. While Artificial Intelligence (AI) has advanced healthcare diagnostics, training Machine Learning (ML) and Deep Learning (DL) models for early detection of rare neurological disorders remains a challenge due to limited patient data. This data scarcity poses a significant public health issue. Meta_Trans Learning (M_TAL), which integrates Meta-Learning (M_tL) and Transfer Learning (TL), offers a promising solution by leveraging small datasets to extract expert patterns, generalize findings, and reduce AI bias in healthcare. This research systematically reviews studies from 2018 to 2024 to explore how ML and M_TAL techniques are applied in diagnosing NDD, NBD, and ND_e disorders. It also provides statistical and parametric analysis of ML and DL methods for neurological disorder diagnosis. Lastly, the study introduces a MRI-based ND_e-M_TAL framework to aid healthcare professionals in early detection of rare neuro disorders, aiming to enhance diagnostic accuracy and advance healthcare practices.

Large language models (LLMs) can generate outputs understandable by humans, such as answers to medical questions and radiology reports. With the rapid development of LLMs, clinicians face a growing challenge in determining the most suitable algorithms to support their work. We aimed to provide clinicians and other health care practitioners with systematic guidance in selecting an LLM that is relevant and appropriate to their needs and facilitate the integration process of LLMs in health care. We conducted a literature search of full-text publications in English on clinical applications of LLMs published between January 1, 2022, and March 31, 2025, on PubMed, ScienceDirect, Scopus, and IEEE Xplore. We excluded papers from journals below a set citation threshold, as well as papers that did not focus on LLMs, were not research based, or did not involve clinical applications. We also conducted a literature search on arXiv within the same investigated period and included papers on the clinical applications of innovative multimodal LLMs. This led to a total of 270 studies. We collected 330 LLMs and recorded their application frequency in clinical tasks and frequency of best performance in their context. On the basis of a 5-stage clinical workflow, we found that stages 2, 3, and 4 are key stages in the clinical workflow, involving numerous clinical subtasks and LLMs. However, the diversity of LLMs that may perform optimally in each context remains limited. GPT-3.5 and GPT-4 were the most versatile models in the 5-stage clinical workflow, applied to 52% (29/56) and 71% (40/56) of the clinical subtasks, respectively, and they performed best in 29% (16/56) and 54% (30/56) of the clinical subtasks, respectively. General-purpose LLMs may not perform well in specialized areas as they often require lightweight prompt engineering methods or fine-tuning techniques based on specific datasets to improve model performance. Most LLMs with multimodal abilities are closed-source models and, therefore, lack of transparency, model customization, and fine-tuning for specific clinical tasks and may also pose challenges regarding data protection and privacy, which are common requirements in clinical settings. In this review, we found that LLMs may help clinicians in a variety of clinical tasks. However, we did not find evidence of generalist clinical LLMs successfully applicable to a wide range of clinical tasks. Therefore, their clinical deployment remains challenging. On the basis of this review, we propose an interactive online guideline for clinicians to select suitable LLMs by clinical task. With a clinical perspective and free of unnecessary technical jargon, this guideline may be used as a reference to successfully apply LLMs in clinical settings.

As non-surgical therapies gain acceptance in head and neck tumors, the importance of imaging has increased. New therapeutic methods (in radiation therapy, targeted biological therapy, immunotherapy) need better tumor characterization and prognostic information along with the accurate anatomy. Magnetic resonance imaging (MRI) has become the gold standard in head and neck cancer evaluation not only for staging but also for assessing tumor response, posttreatment status and complications, as well as for finding residual or recurrent tumor. Multiparametric anatomical and functional MRI (MP-MRI) is a true cancer imaging biomarker providing, in addition to high resolution tumor anatomy, more molecular and functional, qualitative and quantitative data using diffusion- weighted MRI (DW-MRI) and perfusion-dynamic contrast enhanced MRI (P-DCE-MRI), can improve the assessment of biological target volume and determine treatment response. DW-MRI provides information at the cellular level about the cell density and the integrity of the plasma membrane, based on water movement. P-DCE-MRI provides useful hemodynamic information about tissue vascularity and vascular permeability. Recent studies have shown promising results using radiomics features, MP-MRI has opened new perspectives in oncologic imaging with better realization of the latest technological advances with the help of artificial intelligence.

Neurogenetic disorders (NeD) are a group of neurological conditions resulting from inherited genetic defects. By affecting the normal functioning of the nervous system, these diseases lead to serious problems in movement, cognition, and other body functions. In recent years, machine learning (ML) approaches have proven highly effective, enabling the analysis and processing of vast amounts of medical data. By analyzing genetic data, medical imaging, and other clinical data, these techniques can contribute to early diagnosis and more effective treatment of NeD. However, using these approaches is challenged by issues including data variability, model explainability, and the requirement for interdisciplinary collaboration. This paper investigates the impact of ML on healthcare diagnosis and care of common NeD, such as Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), and Multiple Sclerosis disease (MSD). The purpose of this research is to determine the opportunities and challenges of using these techniques in the field of neurogenetic medicine. Our findings show that using ML can increase the detection accuracy by 85 % and reduce the detection time by 60 %. Additionally, the use of these techniques in predicting patient prognosis has been 70 % more accurate than traditional methods. Ultimately, this research will enable medical professionals and researchers to leverage ML approaches in advancing the diagnostic and therapeutic processes of NeD by identifying the opportunities and challenges.

Nasal bone fractures represent the most common facial skeletal injury, challenging both function and aesthetics. This Preferred Reporting Items for Systematic Reviews and Meta-Analyses-based review analyzed 23 studies published within the past 5 years, selected from 998 records retrieved from PubMed, Embase, and Web of Science. Data from 1780 participants were extracted, focusing on diagnostic methods, surgical techniques, anesthesia protocols, and long-term outcomes. Ultrasound and artificial intelligence-based algorithms improved diagnostic accuracy, while telephone triage streamlined necessary encounters. Navigation-assisted reduction, ballooning, and septal reduction with polydioxanone plates improved outcomes. Anesthetic approaches ranged from local nerve blocks to general anesthesia with intraoperative administration of lidocaine, alongside techniques to manage pain from nasal pack removal postoperatively. Long-term follow-up demonstrated improved quality of life, breathing function, and aesthetic satisfaction with timely and individualized treatment. This review highlights the trend toward personalized, technology-assisted approaches in nasal fracture management, highlighting areas for future research.

In the rapidly evolving field of dental intelligent healthcare, where Artificial Intelligence (AI) plays a pivotal role, the demand for multimodal datasets is critical. Existing public datasets are primarily composed of single-modal data, predominantly dental radiographs or scans, which limits the development of AI-driven applications for intelligent dental treatment. In this paper, we collect a MultiModal Dental (MMDental) dataset to address this gap. MMDental comprises data from 660 patients, including 3D Cone-beam Computed Tomography (CBCT) images and corresponding detailed expert medical records with initial diagnoses and follow-up documentation. All CBCT scans are conducted under the guidance of professional physicians, and all patient records are reviewed by senior doctors. To the best of our knowledge, this is the first and largest dataset containing 3D CBCT images of teeth with corresponding medical records. Furthermore, we provide a comprehensive analysis of the dataset by exploring patient demographics, prevalence of various dental conditions, and the disease distribution across age groups. We believe this work will be beneficial for further advancements in dental intelligent treatment.

The integration of artificial intelligence (AI) into clinical practice, particularly within radiology, nuclear medicine and radiation oncology, is transforming diagnostic and therapeutic processes. AI-driven tools, especially in deep learning and machine learning, have shown remarkable potential in enhancing image recognition, analysis and decision-making. This technological advancement allows for the automation of routine tasks, improved diagnostic accuracy, and the reduction of human error, leading to more efficient workflows. Moreover, the successful implementation of AI in healthcare requires comprehensive education and training for young clinicians, with a pressing need to incorporate AI into residency programmes, ensuring that future specialists are equipped with traditional skills and a deep understanding of AI technologies and their clinical applications. This includes knowledge of software, data analysis, imaging informatics and ethical considerations surrounding AI use in medicine. By fostering interdisciplinary integration and emphasising AI education, healthcare professionals can fully harness AI's potential to improve patient outcomes and advance the field of medical imaging and therapy. This review aims to evaluate how AI influences radiology, nuclear medicine and radiation oncology, while highlighting the necessity for specialised AI training in medical education to ensure its successful clinical integration.