Tractography is a challenging process with complex rules, driving continuous algorithmic evolution to address its challenges. Meanwhile, deep learning has tackled similarly difficult tasks, such as mastering the Go board game and animating sophisticated robots. Given its transformative impact in these areas, deep learning has the potential to revolutionize tractography within the framework of existing rules. This work provides a brief summary of recent advances and challenges in deep learning-based tractography computing and analysis.

With the advancement of computer technology and imaging equipment, ultrasound has emerged as a crucial tool in breast cancer diagnosis. To gain deeper insights into the research landscape of ultrasound in breast cancer diagnosis, this study employed bibliometric methods for a comprehensive analysis spanning from 2004 to 2024, analyzing 3523 articles from 2176 institutions in 82 countries/regions. Over this period, publications on ultrasound diagnosis of breast cancer showed a fluctuating growth trend from 2004 to 2024. Notably, China, Seoul National University and Kim EK emerged as leading contributors in ultrasound for breast cancer detection, with the most published and cited journals being Ultrasound Med Biol and Radiology. The research spots in this area included "breast lesion", "dense breast" and "breast-conserving surgery", while "machine learning", "ultrasonic imaging", "convolutional neural network", "case report", "pathological complete response", "deep learning", "artificial intelligence" and "classification" are anticipated to become future research frontiers. This groundbreaking bibliometric analysis and visualization of ultrasonic breast cancer diagnosis publications offer clinical medical professionals a reliable research focus and direction.

As one of the leading causes of death worldwide, early detection of lung disease is a very important step to improve the effectiveness of treatment. By using medical image data, such as X-ray or CT-scan, classification of lung disease can be done. Deep learning methods have been widely used to recognize complex patterns in medical images, but this approach has the constraints of requiring large data variations and high computing resources. In overcoming these constraints, the lightweight architecture in deep learning can provide a more efficient solution based on the number of parameters and computing time. This method can be applied to devices with low processor specifications on portable devices such as mobile phones. This article presents a comprehensive review of 23 research studies published between 2020 and 2025, focusing on various lightweight architectures and optimization techniques aimed at improving the accuracy of lung disease detection. The results show that these models are able to significantly reduce parameter sizes, resulting in faster computation times while maintaining competitive accuracy compared to traditional deep learning architectures. From the research that has been done, it can be seen that SqueezeNet applied on public COVID-19 datasets is the best basic architecture with high accuracy, and the number of parameters is 570 thousand, which is very low. On the other hand, UNet requires 31.07 million parameters, and SegNet requires 29.45 million parameters trained on CT scan images from Italian Society of Medical and Interventional Radiology and Radiopedia, so it is less efficient. For the combination method, EfficientNetV2 and Extreme Learning Machine (ELM) are able to achieve the highest accuracy of 98.20 % and can significantly reduce parameters. The worst performance is shown by VGG and UNet with a decrease in accuracy from 91.05 % to 87 % and an increase in the number of parameters. It can be concluded that the lightweight architecture can be applied to medical image classification in the diagnosis of lung disease quickly and efficiently on devices with limited specifications.

To synthesize current evidence on prognostic factors, tools, and strategies influencing functional outcomes in patients with traumatic brain injury (TBI), with a focus on the acute and postacute phases of care. Key early predictors such as Glasgow Coma Scale (GCS) scores, pupillary reactivity, and computed tomography (CT) imaging findings remain fundamental in guiding clinical decision-making. Prognostic models like IMPACT and CRASH enhance early risk stratification, while outcome measures such as the Glasgow Outcome Scale-Extended (GOS-E) provide structured long-term assessments. Despite their utility, heterogeneity in assessment approaches and treatment protocols continues to limit consistency in outcome predictions. Recent advancements highlight the value of fluid biomarkers like neurofilament light chain (NFL) and glial fibrillary acidic protein (GFAP), which offer promising avenues for improved accuracy. Additionally, artificial intelligence models are emerging as powerful tools to integrate complex datasets and refine individualized outcome forecasting. Neurological prognostication after TBI is evolving through the integration of clinical, radiological, molecular, and computational data. Although standardized models and scales remain foundational, emerging technologies and therapies - such as biomarkers, machine learning, and neurostimulants - represent a shift toward more personalized and actionable strategies to optimize recovery and long-term function.

Prompt diagnosis of giant cell arteritis (GCA) with ultrasound is crucial for preventing severe ocular and other complications, yet expertise in ultrasound performance is scarce. The development of an artificial intelligence (AI)-based assistant that facilitates ultrasound image classification and helps to diagnose GCA early promises to close the existing gap. In the projection of the planned AI, this study investigates the minimum image resolution required for human experts to reliably classify ultrasound images of arteries commonly affected by GCA for the presence or absence of GCA. Thirty-one international experts in GCA ultrasonography participated in a web-based exercise. They were asked to classify 10 ultrasound images for each of 5 vascular segments as GCA, normal, or not able to classify. The following segments were assessed: (1) superficial common temporal artery, (2) its frontal and (3) parietal branches (all in transverse view), (4) axillary artery in transverse view, and 5) axillary artery in longitudinal view. Identical images were shown at different resolutions, namely 32 × 32, 64 × 64, 128 × 128, 224 × 224, and 512 × 512 pixels, thereby resulting in a total of 250 images to be classified by every study participant. Classification performance improved with increasing resolution up to a threshold, plateauing at 224 × 224 pixels. At 224 × 224 pixels, the overall classification sensitivity was 0.767 (95% CI, 0.737-0.796), and specificity was 0.862 (95% CI, 0.831-0.888). A resolution of 224 × 224 pixels ensures reliable human expert classification and aligns with the input requirements of many common AI-based architectures. Thus, the results of this study substantially guide projected AI development.

Following the successful paradigm shift of large language models, leveraging pre-training on a massive corpus of data and fine-tuning on different downstream tasks, generalist models have made their foray into computer vision. The introduction of Segment Anything Model (SAM) set a milestone on segmentation of natural images, inspiring the design of a multitude of architectures for medical image segmentation. In this survey we offer a comprehensive and in-depth investigation on generalist models for medical image segmentation. We start with an introduction on the fundamentals concepts underpinning their development. Then, we provide a taxonomy on the different declinations of SAM in terms of zero-shot, few-shot, fine-tuning, adapters, on the recent SAM 2, on other innovative models trained on images alone, and others trained on both text and images. We thoroughly analyze their performances at the level of both primary research and best-in-literature, followed by a rigorous comparison with the state-of-the-art task-specific models. We emphasize the need to address challenges in terms of compliance with regulatory frameworks, privacy and security laws, budget, and trustworthy artificial intelligence (AI). Finally, we share our perspective on future directions concerning synthetic data, early fusion, lessons learnt from generalist models in natural language processing, agentic AI and physical AI, and clinical translation.

Foundation models (FMs) are changing the way medical images are analyzed by learning from large collections of unlabeled data. Instead of relying on manually annotated examples, FMs are pre-trained to learn general-purpose visual features that can later be adapted to specific clinical tasks with little additional supervision. In this review, we examine how FMs are being developed and applied in pathology, radiology, and ophthalmology, drawing on evidence from over 150 studies. We explain the core components of FM pipelines, including model architectures, self-supervised learning methods, and strategies for downstream adaptation. We also review how FMs are being used in each imaging domain and compare design choices across applications. Finally, we discuss key challenges and open questions to guide future research.

Machine learning drives osteoporosis detection and screening with higher clinical accuracy and accessibility than traditional osteoporosis screening tools. This review takes a step-by-step view of machine learning for osteoporosis detection, providing insights into today's osteoporosis detection and the outlook for the future. The early diagnosis and risk detection of osteoporosis have always been crucial and challenging issues in the medical field. With the in-depth application of artificial intelligence technology, especially machine learning technology in the medical field, significant breakthroughs have been made in the application of early diagnosis and risk detection of osteoporosis. Machine learning is a multidimensional technical system that encompasses a wide variety of algorithm types. Machine learning algorithms have become relatively mature and developed over many years in medical data processing. They possess stable and accurate detection performance, laying a solid foundation for the detection and diagnosis of osteoporosis. As an essential part of the machine learning technical system, deep-learning algorithms are complex algorithm models based on artificial neural networks. Due to their robust image recognition and feature extraction capabilities, deep learning algorithms have become increasingly mature in the early diagnosis and risk assessment of osteoporosis in recent years, opening new ideas and approaches for the early and accurate diagnosis and risk detection of osteoporosis. This paper reviewed the latest research over the past decade, ranging from relatively basic and widely adopted machine learning algorithms combined with clinical data to more advanced deep learning techniques integrated with imaging data such as X-ray, CT, and MRI. By analyzing the application of algorithms at different stages, we found that these basic machine learning algorithms performed well when dealing with single structured data but encountered limitations when handling high-dimensional and unstructured imaging data. On the other hand, deep learning can significantly improve detection accuracy. It does this by automatically extracting image features, especially in image histological analysis. However, it faces challenges. These include the "black-box" problem, heavy reliance on large amounts of labeled data, and difficulties in clinical interpretability. These issues highlighted the importance of model interpretability in future machine learning research. Finally, we expect to develop a predictive model in the future that combines multimodal data (such as clinical indicators, blood biochemical indicators, imaging data, and genetic data) integrated with electronic health records and machine learning techniques. This model aims to present a skeletal health monitoring system that is highly accessible, personalized, convenient, and efficient, furthering the early detection and prevention of osteoporosis.

Foundation models (FMs) are changing the way medical images are analyzed by learning from large collections of unlabeled data. Instead of relying on manually annotated examples, FMs are pre-trained to learn general-purpose visual features that can later be adapted to specific clinical tasks with little additional supervision. In this review, we examine how FMs are being developed and applied in pathology, radiology, and ophthalmology, drawing on evidence from over 150 studies. We explain the core components of FM pipelines, including model architectures, self-supervised learning methods, and strategies for downstream adaptation. We also review how FMs are being used in each imaging domain and compare design choices across applications. Finally, we discuss key challenges and open questions to guide future research.