Augmented reality (AR) technology holds significant promise for enhancing surgical navigation in needle-based procedures such as biopsies and ablations. However, most existing AR systems rely on patient-specific markers, which disrupt clinical workflows and require time-consuming preoperative calibrations, thereby hindering operational efficiency and precision. We developed a novel multi-camera AR navigation system that eliminates the need for patient-specific markers by utilizing ceiling-mounted markers mapped to fixed medical imaging devices. A hierarchical optimization framework integrates both marker mapping and multi-camera calibration. Deep learning techniques are employed to enhance marker detection and registration accuracy. Additionally, a vision-based pose compensation method is implemented to mitigate errors caused by patient movement, improving overall positional accuracy. Validation through phantom experiments and simulated clinical scenarios demonstrated an average puncture accuracy of 3.72 ± 1.21 mm. The system reduced needle placement time by 20 s compared to traditional marker-based methods. It also effectively corrected errors induced by patient movement, with a mean positional error of 0.38 pixels and an angular deviation of 0.51 <math xmlns="http://www.w3.org/1998/Math/MathML"><mmultiscripts><mrow></mrow> <mrow></mrow> <mo>∘</mo></mmultiscripts> </math> . These results highlight the system's precision, adaptability, and reliability in realistic surgical conditions. This marker-free AR guidance system significantly streamlines surgical workflows while enhancing needle navigation accuracy. Its simplicity, cost-effectiveness, and adaptability make it an ideal solution for both high- and low-resource clinical environments, offering the potential for improved precision, reduced procedural time, and better patient outcomes.

Neuroimaging plays a crucial role in diagnosing multiple system atrophy and monitoring progressive neurodegeneration in this fatal disease. Advanced MRI techniques and post-processing methods have demonstrated significant volume loss and microstructural changes in brain regions well known to be affected by MSA pathology. These observations can be exploited to support the differential diagnosis of MSA distinguishing it from Parkinson's disease and progressive supranuclear palsy with high sensitivity and specificity. Longitudinal studies reveal aggressive neurodegeneration in MSA, with notable atrophy rates in the cerebellum, pons, and putamen. Radiotracer imaging using PET and SPECT has shown characteristic disease-related patterns, aiding in differential diagnosis and tracking disease progression. Future research should focus on early diagnosis, particularly in prodromal stages, and the development of reliable biomarkers for clinical trials. Combining different neuroimaging modalities and machine learning algorithms can enhance diagnostic precision and provide a comprehensive understanding of MSA pathology.

This study aimed to develop and evaluate eight machine learning models based on multimodal ultrasound to precisely predict of diabetic tibial neuropathy (DTN) in patients. Additionally, the SHapley Additive exPlanations(SHAP)framework was introduced to quantify the importance of each feature variable, providing a precise and noninvasive assessment tool for DTN patients, optimizing clinical management strategies, and enhancing patient prognosis. A prospective analysis was conducted using multimodal ultrasound and clinical data from 255 suspected DTN patients who visited the Second Affiliated Hospital of Fujian Medical University between January 2024 and November 2024. Key features were selected using Least Absolute Shrinkage and Selection Operator (LASSO) regression. Predictive models were constructed using Extreme Gradient Boosting (XGB), Logistic Regression, Support Vector Machines, k-Nearest Neighbors, Random Forest, Decision Tree, Naïve Bayes, and Neural Network. The SHAP method was employed to refine model interpretability. Furthermore, in order to verify the generalization degree of the model, this study also collected 135 patients from three other tertiary hospitals for external test. LASSO regression identified Echo intensity(EI), Cross-sectional area (CSA), Mean elasticity value(Emean), Superb microvascular imaging(SMI), and History of smoking were key features for DTN prediction. The XGB model achieved an Area Under the Curve (AUC) of 0.94, 0.83 and 0.79 in the training, internal test and external test sets, respectively. SHAP analysis highlighted the ranking significance of EI, CSA, Emean, SMI, and History of smoking. Personalized prediction explanations provided by theSHAP values demonstrated the contribution of each feature to the final prediction, and enhancing model interpretability. Furthermore, decision plots depicted how different features influenced mispredictions, thereby facilitating further model optimization or feature adjustment. This study proposed a DTN prediction model based on machine-learning algorithms applied to multimodal ultrasound data. The results indicated the superior performance of the XGB model and its interpretability was enhanced using SHAP analysis. This cost-effective and user-friendly approach provides potential support for personalized treatment and precision medicine for DTN.

Artificial intelligence has become an impressive force manifesting itself in the radiology field, improving workflows, and influencing clinical decision-making. With this increasing presence, a closer look at how residents can be properly exposed to this technology is needed. Within this paper, we aim to discuss the three pillars central to a trainee's experience including education on AI, AI-Education tools, and clinical implementation of AI. An already overcrowded clinical residency curricula makes little room for a thorough AI education; the challenge of which may be overcome through longitudinal distinct educational tracks during residency or external courses offered through a variety of societies. In addition to teaching the fundamentals of AI, programs which offer education tools utilizing AI will improve on antiquated clinical curricula. These education tools are a growing field in research and industry offering a variety of unique opportunities to promote active inquiry, improved comprehension and overall clinical competence. The near 700 FDA-approved AI clinical tools almost guarantees that residents will be exposed to this technology which may have mixed effects on education, although more research needs to be done to further elucidate this challenge. Ethical considerations, including algorithmic bias, liability, and post-deployment monitoring, highlight the need for structured instruction and mentorship. As AI continues to evolve, residency programs must prioritize evidence-based, adaptable curricula to prepare future radiologists to critically assess, utilize, and contribute to AI advancements, ensuring that these tools complement rather than undermine clinical expertise.

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.

There has been increasing interest in the generation of high-quality, realistic synthetic medical images in recent years. Such synthetic datasets can mitigate the scarcity of public datasets for artificial intelligence research, and can also be used for educational purposes. In this paper, we propose a combination of diffusion-based generation (PanoDiff) and Super-Resolution (SR) for generating synthetic dental panoramic radiographs (PRs). The former generates a low-resolution (LR) seed of a PR (256 X 128) which is then processed by the SR model to yield a high-resolution (HR) PR of size 1024 X 512. For SR, we propose a state-of-the-art transformer that learns local-global relationships, resulting in sharper edges and textures. Experimental results demonstrate a Frechet inception distance score of 40.69 between 7243 real and synthetic images (in HR). Inception scores were 2.55, 2.30, 2.90 and 2.98 for real HR, synthetic HR, real LR and synthetic LR images, respectively. Among a diverse group of six clinical experts, all evaluating a mixture of 100 synthetic and 100 real PRs in a time-limited observation, the average accuracy in distinguishing real from synthetic images was 68.5% (with 50% corresponding to random guessing).

Foundation models like CLIP and SAM have transformed computer vision and medical imaging via low-shot transfer learning. However, deployment of these models hindered by two key challenges: \textit{distribution shift} between training and test data, and \textit{confidence misalignment} that leads to overconfident incorrect predictions. These issues manifest differently in vision-language classification and medical segmentation tasks, yet existing solutions remain domain-specific. We propose \textit{StaRFM}, a unified framework addressing both challenges. It introduces a Fisher information penalty (FIP), extended to 3D medical data via patch-wise regularization, to reduce covariate shift in CLIP and SAM embeddings. Additionally, a confidence misalignment penalty (CMP), reformulated for voxel-level predictions, calibrates uncertainty in segmentation tasks. We theoretically derive PAC-Bayes bounds showing FIP controls generalization via the Fisher-Rao norm, while CMP minimizes calibration error through Brier score optimization. StaRFM shows consistent performance like \texttt{+}3.5\% accuracy and 28\% lower ECE on 19 vision datasets (e.g., ImageNet, Office-Home), 84.7\% DSC and 4.8mm HD95 in medical segmentation (e.g., BraTS, ATLAS), and 40\% lower cross-domain performance gap compared to prior benchmarking methods. The framework is plug-and-play, requiring minimal architectural changes for seamless integration with foundation models. Code and models will be released at https://anonymous.4open.science/r/StaRFM-C0CD/README.md

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.

Cone beam computed tomography (CBCT) has revolutionized dental imaging due to its high spatial resolution and ability to provide detailed three-dimensional reconstructions of dental structures. This study introduces an innovative CBCT image processing method using an oriented object detection approach integrated with a Region of Interest (RoI) Transformer. This study addresses the challenge of accurate tooth detection and classification in PAN derived from CBCT, introducing an innovative oriented object detection approach, which has not been previously applied in dental imaging. This method better aligns with the natural growth patterns of teeth, allowing for more accurate detection and classification of molars, premolars, canines, and incisors. By integrating RoI transformer, the model demonstrates relatively acceptable performance metrics compared to conventional horizontal detection methods, while also offering enhanced visualization capabilities. Furthermore, post-processing techniques, including distance and grayscale value constraints, are employed to correct classification errors and reduce false positives, especially in areas with missing teeth. The experimental results indicate that the proposed method achieves an accuracy of 98.48%, a recall of 97.21%, an F1 score of 97.21%, and an mAP of 98.12% in tooth detection. The proposed method enhances the accuracy of tooth detection in CBCT-derived PAN by reducing background interference and improving the visualization of tooth orientation.

In medical image segmentation, convolutional neural networks (CNNs) and transformers are dominant. For CNNs, given the local receptive fields of convolutional layers, long-range spatial correlations are captured through consecutive convolutions and pooling. However, as the computational cost and memory footprint can be prohibitively large, 3D models can only afford fewer layers than 2D models with reduced receptive fields and abstract levels. For transformers, although long-range correlations can be captured by multi-head attention, its quadratic complexity with respect to input size is computationally demanding. Therefore, either model may require input size reduction to allow more filters and layers for better segmentation. Nevertheless, given their discrete nature, models trained with patch-wise training or image downsampling may produce suboptimal results when applied on higher resolutions. To address this issue, here we propose the resolution-robust HNOSeg-XS architecture. We model image segmentation by learnable partial differential equations through the Fourier neural operator which has the zero-shot super-resolution property. By replacing the Fourier transform by the Hartley transform and reformulating the problem in the frequency domain, we created the HNOSeg-XS model, which is resolution robust, fast, memory efficient, and extremely parameter efficient. When tested on the BraTS'23, KiTS'23, and MVSeg'23 datasets with a Tesla V100 GPU, HNOSeg-XS showed its superior resolution robustness with fewer than 34.7k model parameters. It also achieved the overall best inference time (< 0.24 s) and memory efficiency (< 1.8 GiB) compared to the tested CNN and transformer models¹.