Recent advancements in diagnostic technologies have significantly transformed the landscape of respiratory medicine, aiming for early detection, improved specificity and personalized therapeutic strategies. Innovations in imaging such as multi-slice computed tomography (CT) scanners, high-resolution CT and magnetic resonance imaging (MRI) have revolutionized our ability to visualize and assess the structural and functional aspects of the respiratory system. These techniques are complemented by breakthroughs in molecular biology that have identified specific biomarkers and genetic determinants of respiratory diseases, enabling targeted diagnostic approaches. Additionally, functional tests including spirometry and exercise testing continue to provide valuable insights into pulmonary function and capacity. The integration of artificial intelligence is poised to further refine these diagnostic tools, enhancing their accuracy and efficiency. The present narrative review explores these developments and their impact on the management and outcomes of respiratory conditions, underscoring the ongoing shift towards more precise and less invasive diagnostic modalities in respiratory medicine.

Deformable registration is essential in medical image analysis, especially for handling various multi- and mono-modal registration tasks in neuroimaging. Existing studies lack exploration of brain MR-CT registration, and face challenges in both accuracy and efficiency improvements of learning-based methods. To enlarge the practice of multi-modal registration in brain, we present SR-Reg, a new benchmark dataset comprising 180 volumetric paired MR-CT images and annotated anatomical regions. Building on this foundation, we introduce MambaMorph, a novel deformable registration network based on an efficient state space model Mamba for global feature learning, with a fine-grained feature extractor for low-level embedding. Experimental results demonstrate that MambaMorph surpasses advanced ConvNet-based and Transformer-based networks across several multi- and mono-modal tasks, showcasing impressive enhancements of efficacy and efficiency. Code and dataset are available at https://github.com/mileswyn/MambaMorph.

The US healthcare system faces significant challenges, including clinician burnout, operational inefficiencies, and concerns about patient safety. Artificial intelligence (AI), particularly generative AI, has the potential to address these challenges, but its adoption, effectiveness, and barriers to implementation are not well understood. To evaluate the current state of AI adoption in US healthcare systems, assess successes and barriers to implementation during the early generative AI era. This cross-sectional survey was conducted in Fall 2024, and included 67 health systems members of the Scottsdale Institute, a collaborative of US non-profit healthcare organizations. Forty-three health systems completed the survey (64% response rate). Respondents provided data on the deployment status and perceived success of 37 AI use cases across 10 categories. The primary outcomes were the extent of AI use case development, piloting, or deployment, the degree of reported success for AI use cases, and the most significant barriers to adoption. Across the 43 responding health systems, AI adoption and perceptions of success varied significantly. Ambient Notes, a generative AI tool for clinical documentation, was the only use case with 100% of respondents reporting adoption activities, and 53% reported a high degree of success with using AI for Clinical Documentation. Imaging and radiology emerged as the most widely deployed clinical AI use case, with 90% of organizations reporting at least partial deployment, although successes with diagnostic use cases were limited. Similarly, many organizations have deployed AI for clinical risk stratification such as early sepsis detection, but only 38% report high success in this area. Immature AI tools were identified a significant barrier to adoption, cited by 77% of respondents, followed by financial concerns (47%) and regulatory uncertainty (40%). Ambient Notes is rapidly advancing in US healthcare systems and demonstrating early success. Other AI use cases show varying degrees of adoption and success, constrained by barriers such as immature AI tools, financial concerns, and regulatory uncertainty. Addressing these challenges through robust evaluations, shared strategies, and governance models will be essential to ensure effective integration and adoption of AI into healthcare practice.

The reliance on computationally intensive U-Net and Transformer architectures significantly limits their accessibility in low-resource environments, creating a technological divide that hinders global healthcare equity, especially in medical diagnostics and treatment planning. This divide is most pronounced in low- and middle-income countries, primary care facilities, and conflict zones. We introduced MED-NCA, Neural Cellular Automata (NCA) based segmentation models characterized by their low parameter count, robust performance, and inherent quality control mechanisms. These features drastically lower the barriers to high-quality medical image analysis in resource-constrained settings, allowing the models to run efficiently on hardware as minimal as a Raspberry Pi or a smartphone. Building upon the foundation laid by MED-NCA, this paper extends its validation across eight distinct anatomies, including the hippocampus and prostate (MRI, 3D), liver and spleen (CT, 3D), heart and lung (X-ray, 2D), breast tumor (Ultrasound, 2D), and skin lesion (Image, 2D). Our comprehensive evaluation demonstrates the broad applicability and effectiveness of MED-NCA in various medical imaging contexts, matching the performance of two magnitudes larger UNet models. Additionally, we introduce NCA-VIS, a visualization tool that gives insight into the inference process of MED-NCA and allows users to test its robustness by applying various artifacts. This combination of efficiency, broad applicability, and enhanced interpretability makes MED-NCA a transformative solution for medical image analysis, fostering greater global healthcare equity by making advanced diagnostics accessible in even the most resource-limited environments.

Medical image segmentation is a pivotal task within the realms of medical image analysis and computer vision. While current methods have shown promise in accurately segmenting major regions of interest, the precise segmentation of boundary areas remains challenging. In this study, we propose a novel network architecture named CTO, which combines Convolutional Neural Networks (CNNs), Vision Transformer (ViT) models, and explicit edge detection operators to tackle this challenge. CTO surpasses existing methods in terms of segmentation accuracy and strikes a better balance between accuracy and efficiency, without the need for additional data inputs or label injections. Specifically, CTO adheres to the canonical encoder-decoder network paradigm, with a dual-stream encoder network comprising a mainstream CNN stream for capturing local features and an auxiliary StitchViT stream for integrating long-range dependencies. Furthermore, to enhance the model's ability to learn boundary areas, we introduce a boundary-guided decoder network that employs binary boundary masks generated by dedicated edge detection operators to provide explicit guidance during the decoding process. We validate the performance of CTO through extensive experiments conducted on seven challenging medical image segmentation datasets, namely ISIC 2016, PH2, ISIC 2018, CoNIC, LiTS17, BraTS, and BTCV. Our experimental results unequivocally demonstrate that CTO achieves state-of-the-art accuracy on these datasets while maintaining competitive model complexity. The codes have been released at: CTO.

Traditional medical image generation often lacks patient-specific clinical information, limiting its clinical utility despite enhancing downstream task performance. In contrast, medical image translation precisely converts images from one modality to another, preserving both anatomical structures and cross-modal features, thus enabling efficient and accurate modality transfer and offering unique advantages for model development and clinical practice. This paper reviews the latest advancements in deep learning(DL)-based medical image translation. Initially, it elaborates on the diverse tasks and practical applications of medical image translation. Subsequently, it provides an overview of fundamental models, including convolutional neural networks (CNNs), transformers, and state space models (SSMs). Additionally, it delves into generative models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Autoregressive Models (ARs), diffusion Models, and flow Models. Evaluation metrics for assessing translation quality are discussed, emphasizing their importance. Commonly used datasets in this field are also analyzed, highlighting their unique characteristics and applications. Looking ahead, the paper identifies future trends, challenges, and proposes research directions and solutions in medical image translation. It aims to serve as a valuable reference and inspiration for researchers, driving continued progress and innovation in this area.

Although there are relatively few diverse, high-quality medical imaging datasets on which to train computer vision artificial intelligence models, even fewer datasets contain expertly classified observations that can be repurposed to train or test such models. The traditional annotation process is laborious and time-consuming. Repurposing annotations and consolidating similar types of annotations from disparate sources has never been practical. Until recently, the use of natural language processing to convert a clinical radiology report into labels required custom training of a language model for each use case. Newer technologies such as large language models have made it possible to generate accurate and normalized labels at scale, using only clinical reports and specific prompt engineering. The combination of automatically generated labels extracted and normalized from reports in conjunction with foundational image models provides a means to create labels for model training. This article provides a short history and review of the annotation and labeling process of medical images, from the traditional manual methods to the newest semiautomated methods that provide a more scalable solution for creating useful models more efficiently. <b>Keywords:</b> Feature Detection, Diagnosis, Semi-supervised Learning © RSNA, 2025.

Precise segmentation and uncertainty estimation are crucial for error identification and correction in medical diagnostic assistance. Existing methods mainly rely on pixel-wise uncertainty estimations. They (1) neglect the global context, leading to erroneous uncertainty indications, and (2) bring attention interference, resulting in the waste of extensive details and potential understanding confusion. In this paper, we propose a novel structural uncertainty estimation method, based on Convolutional Neural Networks (CNN) and Active Shape Models (ASM), named SU-ASM, which incorporates global shape information for providing precise segmentation and uncertainty estimation. The SU-ASM consists of three components. Firstly, multi-task generation provides multiple outcomes to assist ASM initialization and shape optimization via a multi-task learning module. Secondly, information fusion involves the creation of a Combined Boundary Probability (CBP) and along with a rapid shape initialization algorithm, Key Landmark Template Matching (KLTM), to enhance boundary reliability and select appropriate shape templates. Finally, shape model fitting where multiple shape templates are matched to the CBP while maintaining their intrinsic shape characteristics. Fitted shapes generate segmentation results and structural uncertainty estimations. The SU-ASM has been validated on cardiac ultrasound dataset, ciliary muscle dataset of the anterior eye segment, and the chest X-ray dataset. It outperforms state-of-the-art methods in terms of segmentation and uncertainty estimation.

Schizophrenia is a severe yet treatable mental disorder, and it is diagnosed using a multitude of primary and secondary symptoms. Diagnosis and treatment for each individual depends on the severity of the symptoms. Therefore, there is a need for accurate, personalised assessments. However, the process can be both time-consuming and subjective; hence, there is a motivation to explore automated methods that can offer consistent diagnosis and precise symptom assessments, thereby complementing the work of healthcare practitioners. Machine Learning has demonstrated impressive capabilities across numerous domains, including medicine; the use of Machine Learning in patient assessment holds great promise for healthcare professionals and patients alike, as it can lead to more consistent and accurate symptom estimation. This survey reviews methodologies utilising Machine Learning for diagnosing and assessing schizophrenia. Contrary to previous reviews that primarily focused on binary classification, this work recognises the complexity of the condition and, instead, offers an overview of Machine Learning methods designed for fine-grained symptom estimation. We cover multiple modalities, namely Medical Imaging, Electroencephalograms and Audio-Visual, as the illness symptoms can manifest in a patient's pathology and behaviour. Finally, we analyse the datasets and methodologies used in the studies and identify trends, gaps, as opportunities for future research.

Federated Learning (FL) has emerged as a promising approach for collaborative medical image analysis while preserving data privacy, making it particularly suitable for radiomics tasks. This paper presents a systematic meta-analysis of recent surveys on Federated Learning in Medical Imaging (FL-MI), published in reputable venues over the past five years. We adopt the PRISMA methodology, categorizing and analyzing the existing body of research in FL-MI. Our analysis identifies common trends, challenges, and emerging strategies for implementing FL in medical imaging, including handling data heterogeneity, privacy concerns, and model performance in non-IID settings. The paper also highlights the most widely used datasets and a comparison of adopted machine learning models. Moreover, we examine FL frameworks in FL-MI applications, such as tumor detection, organ segmentation, and disease classification. We identify several research gaps, including the need for more robust privacy protection. Our findings provide a comprehensive overview of the current state of FL-MI and offer valuable directions for future research and development in this rapidly evolving field.