Despite significant advancements in adapting Large Language Models (LLMs) for radiology report generation (RRG), clinical adoption remains challenging due to difficulties in accurately mapping pathological and anatomical features to their corresponding text descriptions. Additionally, semantic agnostic feature extraction further hampers the generation of accurate diagnostic reports. To address these challenges, we introduce Medical Concept Aligned Radiology Report Generation (MCA-RG), a knowledge-driven framework that explicitly aligns visual features with distinct medical concepts to enhance the report generation process. MCA-RG utilizes two curated concept banks: a pathology bank containing lesion-related knowledge, and an anatomy bank with anatomical descriptions. The visual features are aligned with these medical concepts and undergo tailored enhancement. We further propose an anatomy-based contrastive learning procedure to improve the generalization of anatomical features, coupled with a matching loss for pathological features to prioritize clinically relevant regions. Additionally, a feature gating mechanism is employed to filter out low-quality concept features. Finally, the visual features are corresponding to individual medical concepts, and are leveraged to guide the report generation process. Experiments on two public benchmarks (MIMIC-CXR and CheXpert Plus) demonstrate that MCA-RG achieves superior performance, highlighting its effectiveness in radiology report generation.

In recent years, hip arthroscopy has made great progress and has been extended to the treatment of intra-articular or periarticular diseases. However, the complex structure of the hip joint, high technical operation requirements and relatively long learning curve have hindered the popularization and development of hip arthroscopy in China. Therefore, on the one hand, it is necessary to promote the research and training of standardized techniques for the diagnosis of hip disease and the treatment of arthroscopic surgery, so as to improve the safety, effectiveness and popularization of the technology. On the other hand, our organization proactively leverages cutting-edge digitalization and intelligentization technologies, including medical image digitalization, medical big data analytics, artificial intelligence, surgical navigation and robotic control, virtual reality, telemedicine, and 5G communication technology. We conduct a range of innovative research and development initiatives such as intelligent-assisted diagnosis of hip diseases, digital preoperative planning, surgical intelligent navigation and robotic procedures, and smart rehabilitation solutions. These efforts aim to facilitate a digitalization and intelligentization leap in technology and continuously enhance the precision of diagnosis and treatment. In conclusion, standardization promotes the homogenization of diagnosis and treatment, while digitalization and intelligentization facilitate the precision of operations. The synergy of the two lays the foundation for personalized diagnosis and treatment and continuous innovation, ultimately driving the rapid development of hip arthroscopy technology.

Privacy-preserving large language models (PP-LLMs) hold potential for assisting clinicians with documentation. We evaluated a PP-LLM to improve the clinical information on radiology request forms for musculoskeletal magnetic resonance imaging (MRI) and to automate protocoling, which ensures that the most appropriate imaging is performed. The present retrospective study included musculoskeletal MRI radiology request forms that had been randomly collected from June to December 2023. Studies without electronic medical record (EMR) entries were excluded. An institutional PP-LLM (Claude Sonnet 3.5) augmented the original radiology request forms by mining EMRs, and, in combination with rule-based processing of the LLM outputs, suggested appropriate protocols using institutional guidelines. Clinical information on the original and PP-LLM radiology request forms were compared with use of the RI-RADS (Reason for exam Imaging Reporting and Data System) grading by 2 musculoskeletal (MSK) radiologists independently (MSK1, with 13 years of experience, and MSK2, with 11 years of experience). These radiologists established a consensus reference standard for protocoling, against which the PP-LLM and of 2 second-year board-certified radiologists (RAD1 and RAD2) were compared. Inter-rater reliability was assessed with use of the Gwet AC1, and the percentage agreement with the reference standard was calculated. Overall, 500 musculoskeletal MRI radiology request forms were analyzed for 407 patients (202 women and 205 men with a mean age [and standard deviation] of 50.3 ± 19.5 years) across a range of anatomical regions, including the spine/pelvis (143 MRI scans; 28.6%), upper extremity (169 scans; 33.8%) and lower extremity (188 scans; 37.6%). Two hundred and twenty-two (44.4%) of the 500 MRI scans required contrast. The clinical information provided in the PP-LLM-augmented radiology request forms was rated as superior to that in the original requests. Only 0.4% to 0.6% of PP-LLM radiology request forms were rated as limited/deficient, compared with 12.4% to 22.6% of the original requests (p < 0.001). Almost-perfect inter-rater reliability was observed for LLM-enhanced requests (AC1 = 0.99; 95% confidence interval [CI], 0.99 to 1.0), compared with substantial agreement for the original forms (AC1 = 0.62; 95% CI, 0.56 to 0.67). For protocoling, MSK1 and MSK2 showed almost-perfect agreement on the region/coverage (AC1 = 0.96; 95% CI, 0.95 to 0.98) and contrast requirement (AC1 = 0.98; 95% CI, 0.97 to 0.99). Compared with the consensus reference standard, protocoling accuracy for the PP-LLM was 95.8% (95% CI, 94.0% to 97.6%), which was significantly higher than that for both RAD1 (88.6%; 95% CI, 85.8% to 91.4%) and RAD2 (88.2%; 95% CI, 85.4% to 91.0%) (p < 0.001 for both). Musculoskeletal MRI request form augmentation with an institutional LLM provided superior clinical information and improved protocoling accuracy compared with clinician requests and non-MSK-trained radiologists. Institutional adoption of such LLMs could enhance the appropriateness of MRI utilization and patient care. Diagnostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Education for medical imaging technologists or radiographers in regional and rural areas often faces significant challenges due to limited financial, technological, and teaching resources. Generative AI presents a promising solution to overcome these barriers and support the professional development of radiographers. This pilot study aimed to evaluate the educational value of an in-house AI-based imaging simulation tool designed to generate clinically relevant medical images for professional training purposes. In July 2023, a professional development lecture featuring AI-generated clinical imaging content was delivered to students (N = 122/130) and recent graduates (N = 155/532), alongside a pre-lecture survey. Following the session, participants completed a questionnaire comprising structured and open-ended items to assess their understanding, perceptions, and interest in AI within medical imaging education. Survey results indicated that both students and graduates possessed a foundational awareness of AI applications in medical imaging. Graduates demonstrated significantly higher expectations for clinical realism in AI-generated simulations, likely reflecting their clinical experience. Although the simulator's current capabilities are limited in replicating complex diagnostic imaging, participants acknowledged its pedagogical value, particularly in supporting basic anatomical education. Approximately 50% of respondents expressed interest in further developing their AI knowledge and contributing to the research and development of AI-based educational tools. AI-driven imaging simulation tools have the potential to enhance radiography education and reduce teaching barriers. While further development is needed to improve clinical fidelity, such tools can play a valuable role in foundational training and foster learner engagement in AI innovation.

This article reviews advancements in fully automated abdominal CT interpretation over the past decade, with a focus on automated image analysis techniques such as quantitative analysis, computer-aided detection, and disease classification. For each abdominal organ, we review segmentation techniques, assess clinical applications and performance, and explore methods for detecting/classifying associated pathologies. We also highlight cutting-edge AI developments, including foundation models, large language models, and multimodal image analysis. While challenges remain in integrating AI into radiology practice, recent progress underscores its growing potential to streamline workflows, reduce radiologist burnout, and enhance patient care.

Foundation models (FMs) represent a significant evolution in artificial intelligence (AI), impacting diverse fields. Within radiology, this evolution offers greater adaptability, multimodal integration, and improved generalizability compared with traditional narrow AI. Utilizing large-scale pre-training and efficient fine-tuning, FMs can support diverse applications, including image interpretation, report generation, integrative diagnostics combining imaging with clinical/laboratory data, and synthetic data creation, holding significant promise for advancements in precision medicine. However, clinical translation of FMs faces several substantial challenges. Key concerns include the inherent opacity of model decision-making processes, environmental and social sustainability issues, risks to data privacy, complex ethical considerations, such as bias and fairness, and navigating the uncertainty of regulatory frameworks. Moreover, rigorous validation is essential to address inherent stochasticity and the risk of hallucination. This international collaborative effort provides a comprehensive overview of the fundamentals, applications, opportunities, challenges, and prospects of FMs, aiming to guide their responsible and effective adoption in radiology and healthcare.

With the ongoing revolution of artificial intelligence (AI) in medicine, the impact of AI in radiology is more pronounced than ever. An increasing number of technical and clinical AI-focused studies are published each day. As these tools inevitably affect patient care and physician practices, it is crucial that radiologists become more familiar with the leading strategies and underlying principles of AI. Multimodal AI models can combine both imaging and clinical metadata and are quickly becoming a popular approach that is being integrated into the medical ecosystem. This narrative review covers major concepts of multimodal AI through the lens of recent literature. We discuss emerging frameworks, including graph neural networks, which allow for explicit learning from non-Euclidean relationships, and transformers, which allow for parallel computation that scales, highlighting existing literature and advocating for a focus on emerging architectures. We also identify key pitfalls in current studies, including issues with taxonomy, data scarcity, and bias. By informing radiologists and biomedical AI experts about existing practices and challenges, we hope to guide the next wave of imaging-based multimodal AI research.

Low-dose computed tomography (LDCT) reduces radiation exposure but often degrades image quality, potentially compromising diagnostic accuracy. Existing deep learning-based denoising methods focus primarily on pixel-level mappings, overlooking the potential benefits of high-level semantic guidance. Recent advances in vision-language models (VLMs) suggest that language can serve as a powerful tool for capturing structured semantic information, offering new opportunities to improve LDCT reconstruction. In this paper, we introduce LangMamba, a Language-driven Mamba framework for LDCT denoising that leverages VLM-derived representations to enhance supervision from normal-dose CT (NDCT). LangMamba follows a two-stage learning strategy. First, we pre-train a Language-guided AutoEncoder (LangAE) that leverages frozen VLMs to map NDCT images into a semantic space enriched with anatomical information. Second, we synergize LangAE with two key components to guide LDCT denoising: Semantic-Enhanced Efficient Denoiser (SEED), which enhances NDCT-relevant local semantic while capturing global features with efficient Mamba mechanism, and Language-engaged Dual-space Alignment (LangDA) Loss, which ensures that denoised images align with NDCT in both perceptual and semantic spaces. Extensive experiments on two public datasets demonstrate that LangMamba outperforms conventional state-of-the-art methods, significantly improving detail preservation and visual fidelity. Remarkably, LangAE exhibits strong generalizability to unseen datasets, thereby reducing training costs. Furthermore, LangDA loss improves explainability by integrating language-guided insights into image reconstruction and offers a plug-and-play fashion. Our findings shed new light on the potential of language as a supervisory signal to advance LDCT denoising. The code is publicly available on https://github.com/hao1635/LangMamba.

Brain lesions are abnormalities or injuries in brain tissue that are often detectable using magnetic resonance imaging (MRI), which reveals structural changes in the affected areas. This broad definition of brain lesions includes areas of the brain that are irreversibly damaged, as well as areas of brain tissue that are deformed as a result of lesion growth or swelling. Despite the importance of differentiating between damaged and deformed tissue, existing lesion segmentation methods overlook this distinction, labeling both of them as a single anomaly. In this work, we introduce a diffusion model-based framework for analyzing and reversing the brain lesion process. Our pipeline first segments abnormal regions in the brain, then estimates and reverses tissue deformations by restoring displaced tissue to its original position, isolating the core lesion area representing the initial damage. Finally, we inpaint the core lesion area to arrive at an estimation of the pre-lesion healthy brain. This proposed framework reverses a forward lesion growth process model that is well-established in biomechanical studies that model brain lesions. Our results demonstrate improved accuracy in lesion segmentation, characterization, and brain labeling compared to traditional methods, offering a robust tool for clinical and research applications in brain lesion analysis. Since pre-lesion healthy versions of abnormal brains are not available in any public dataset for validation of the reverse process, we simulate a forward model to synthesize multiple lesioned brain images.

Generative AI models have demonstrated strong potential in radiology report generation, but their clinical adoption depends on physician trust. In this study, we conducted a radiology-focused Turing test to evaluate how well attendings and residents distinguish AI-generated reports from those written by radiologists, and how their confidence and decision time reflect trust. we developed an integrated web-based platform comprising two core modules: Report Generation and Report Evaluation. Using the web-based platform, eight participants evaluated 48 anonymized X-ray cases, each paired with two reports from three comparison groups: radiologist vs. AI model 1, radiologist vs. AI model 2, and AI model 1 vs. AI model 2. Participants selected the AI-generated report, rated their confidence, and indicated report preference. Attendings outperformed residents in identifying AI-generated reports (49.9% vs. 41.1%) and exhibited longer decision times, suggesting more deliberate judgment. Both groups took more time when both reports were AI-generated. Our findings highlight the role of clinical experience in AI acceptance and the need for design strategies that foster trust in clinical applications. The project page of the evaluation platform is available at: https://zachatalay89.github.io/Labsite.