High-quality reference standard image data creation by neuroradiology experts for automated clinical tools can be a powerful tool for neuroradiology & artificial intelligence education. We developed a multimodal educational approach for students and trainees during the MICCAI Brain Tumor Segmentation Lighthouse Challenge 2025, a landmark initiative to develop accurate brain tumor segmentation algorithms. Fifty-six medical students & radiology trainees volunteered to annotate brain tumor MR images for the BraTS challenges of 2023 & 2024, guided by faculty-led didactics on neuropathology MRI. Among the 56 annotators, 14 select volunteers were then paired with neuroradiology faculty for guided one-on-one annotation sessions for BraTS 2025. Lectures on neuroanatomy, pathology & AI, journal clubs & data scientist-led workshops were organized online. Annotators & audience members completed surveys on their perceived knowledge before & after annotations & lectures respectively. Fourteen coordinators, each paired with a neuroradiologist, completed the data annotation process, averaging 1322.9+/-760.7 hours per dataset per pair and 1200 segmentations in total. On a scale of 1-10, annotation coordinators reported significant increase in familiarity with image segmentation software pre- and post-annotation, moving from initial average of 6+/-2.9 to final average of 8.9+/-1.1, and significant increase in familiarity with brain tumor features pre- and post-annotation, moving from initial average of 6.2+/-2.4 to final average of 8.1+/-1.2. We demonstrate an innovative offering for providing neuroradiology & AI education through an image segmentation challenge to enhance understanding of algorithm development, reinforce the concept of data reference standard, and diversify opportunities for AI-driven image analysis among future physicians.

Artificial intelligence (AI)-enabled multimodal cardiovascular imaging holds significant promise for improving diagnostic accuracy, enhancing risk stratification, and supporting clinical decision-making. However, its translation into routine practice remains limited by multiple technical, infrastructural, and clinical barriers. This review synthesizes current challenges, including variability in image quality, alignment, and acquisition protocols; scarcity of large, annotated multimodality datasets; interoperability limitations across vendors and institutions; clinical skepticism due to limited prospective validation; and substantial development and implementation costs. Drawing from recent advances, we outline future research priorities to bridge the gap between technical feasibility and clinical utility. Key strategies include developing unified, vendor-agnostic AI models resilient to inter-institutional variability; integrating diverse data types such as genomics, wearable biosensors, and longitudinal clinical records; leveraging reinforcement learning for adaptive decision-support systems; and employing longitudinal imaging fusion for disease tracking and predictive analytics. We emphasize the need for rigorous prospective clinical trials, harmonized imaging standards, and collaborative data-sharing frameworks to ensure robust, equitable, and scalable deployment. Addressing these challenges through coordinated multidisciplinary efforts will be essential to realize the full potential of AI-driven multimodal cardiovascular imaging in advancing precision cardiovascular care.

The application of artificial intelligence to enhance the clinical practice of ultrasound-guided regional anaesthesia is of increasing interest to clinicians, researchers and industry. The lack of standardised reporting for studies in this field hinders the comparability, reproducibility and integration of findings. We aimed to develop a consensus-based reporting guideline for research evaluating artificial intelligence applications for ultrasound scanning in regional anaesthesia. We followed methodology recommended by the EQUATOR Network for the development of reporting guidelines. Review of published literature and expert consultation generated a preliminary list of candidate reporting items. An international, multidisciplinary, modified Delphi process was then undertaken, involving experts from clinical practice, academia and industry. Two rounds of expert consultation were conducted, in which participants evaluated each item for inclusion in a final reporting guideline, followed by an online discussion. A total of 67 experts participated in the first Delphi round, 63 in the second round and 25 in the roundtable consensus meeting. The GRAITE-USRA reporting guideline comprises 40 items addressing key aspects of reporting in artificial intelligence research for ultrasound scanning in regional anaesthesia. Specific items include ultrasound acquisition protocols and operator expertise, which are not covered in existing artificial intelligence reporting guidelines. The GRAITE-USRA reporting guideline provides a minimum set of recommendations for artificial intelligence-related research for ultrasound scanning in regional anaesthesia. Its adoption will promote consistent reporting standards, enhance transparency, improve study reproducibility and ultimately support the effective integration of evidence into clinical practice.

Navigating uncertainty is fundamental to sound clinical decision-making. With the advent of artificial intelligence, mathematical approximations of disease states-expressed as entropy-offer a novel approach to quantify and communicate uncertainty. Although entropy is well established in fields like physics and computer science, its technical complexity has delayed its routine adoption in clinical reasoning. In this narrative review, we adhere to Shannon's definition of entropy from information processing theory and examine how it has been used in clinical decision-making over the last 15 years. Grounding our analysis in decision theory-which frames decisions in terms of states, acts, consequences, and preferences-we evaluated 20 studies that employed entropy. Our findings reveal that entropy is predominantly used to quantify uncertainty rather than directly guiding clinical actions. High-stakes fields such as oncology and radiology have led the way, using entropy to improve diagnostic accuracy and support risk assessment, while applications in neurology and hematology remain largely exploratory. Notably, no study has yet translated entropy into an operational, evidence-based decision-support framework. These results point to entropy's value as a quantitative tool in clinical reasoning, while also highlighting the need for prospective validation and the development of integrated clinical tools.

Esophagogastric varices (EGVs) are a disease that occurs as a complication of the progression of liver cirrhosis, and since bleeding can be fatal, regular endoscopy is necessary. With the development of artificial intelligence (AI) in recent years, it is beginning to be applied to predicting the presence of EGVs, predicting bleeding, and making a diagnosis and prognosis. Based on previous reports, application methods of AI can be classified into the following four categories: (1) noninvasive prediction using clinical data obtained from clinical records such as laboratory data, past history, and present illness, (2) invasive detection and prediction using endoscopy and computed tomography (CT), (3) invasive prediction using multimodal AI (clinical data and endoscopy), (4) invasive virtual measurement on the image of endoscopy and CT. These methods currently allow for the use of AI in the following ways: (1) prediction of EGVs existence, variceal grade, bleeding risk, and survival rate, (2) detection and diagnosis of esophageal varices (EVs), (3) prediction of bleeding within 1 year, (4) prediction of variceal diameter and portal pressure gradient. This review explores current studies on AI applications in assessing EGVs, highlighting their benefits, limitations, and future directions.

Medical imaging plays a crucial role in the accurate diagnosis and prognosis of various medical conditions, with each modality offering unique and complementary insights into the body's structure and function. However, no single imaging technique can capture the full spectrum of necessary information. Data fusion has emerged as a powerful tool to integrate information from different perspectives, including multiple modalities, views, temporal sequences, and spatial scales. By combining data, fusion techniques provide a more comprehensive understanding, significantly enhancing the precision and reliability of clinical analyses. This paper presents an overview of data fusion approaches - covering multi-view, multi-modal, and multi-scale strategies - across imaging modalities such as MRI, CT, PET, SPECT, EEG, and MEG, with a particular emphasis on applications in neurological disorders. Furthermore, we highlight the latest advancements in data fusion methods and key studies published since 2016, illustrating the progress and growing impact of this interdisciplinary field.

Congenital heart diseases (CHD) are a significant cause of neonatal mortality and morbidity. Detecting these abnormalities during pregnancy increases survival rates, enhances prognosis, and improves pregnancy management and quality of life for the affected families. Foetal echocardiography can be considered an accurate method for detecting CHDs. However, the detection of CHDs can be limited by factors such as the sonographer's skill, expertise and patient specific variables. Using artificial intelligence (AI) has the potential to address these challenges, increasing antenatal CHD detection during prenatal care. A scoping review was conducted using Google Scholar, PubMed, and ScienceDirect databases, employing keywords, Boolean operators, and inclusion and exclusion criteria to identify peer-reviewed studies. Thematic mapping and synthesis of the found literature were conducted to review key concepts, research methods and findings. A total of n = 233 articles were identified, after exclusion criteria, the focus was narrowed to n = 7 that met the inclusion criteria. Themes in the literature identified the potential of AI to assist clinicians and trainees, alongside emerging new ethical limitations in ultrasound imaging. AI-based tools in ultrasound imaging offer great potential in assisting sonographers and doctors with decision-making in CHD diagnosis. However, due to the paucity of data and small sample sizes, further research and technological advancements are needed to improve reliability and integrate AI into routine clinical practice. This scoping review identified the reported accuracy and limitations of AI-based tools within foetal cardiac ultrasound imaging. AI has the potential to aid in reducing missed diagnoses, enhance training, and improve pregnancy management. There is a need to understand and address the ethical and legal considerations involved with this new paradigm in imaging.

Lung transplantation remains a critical treatment for end-stage lung diseases, yet it continues to have 1 of the lowest survival rates among solid organ transplants. Despite its life-saving potential, the field faces several challenges, including organ shortages, suboptimal donor matching, and post-transplant complications. The rapidly advancing field of artificial intelligence (AI) offers significant promise in addressing these challenges. Traditional statistical models, such as linear and logistic regression, have been used to predict post-transplant outcomes but struggle to adapt to new trends and evolving data. In contrast, machine learning algorithms can evolve with new data, offering dynamic and updated predictions. AI holds the potential to enhance lung transplantation at multiple stages. In the pre-transplant phase, AI can optimize waitlist management, refine donor selection, and improve donor-recipient matching, and enhance diagnostic imaging by harnessing vast datasets. Post-transplant, AI can help predict allograft rejection, improve immunosuppressive management, and better forecast long-term patient outcomes, including quality of life. However, the integration of AI in lung transplantation also presents challenges, including data privacy concerns, algorithmic bias, and the need for external clinical validation. This review explores the current state of AI in lung transplantation, summarizes key findings from recent studies, and discusses the potential benefits, challenges, and ethical considerations in this rapidly evolving field, highlighting future research directions.

Large language models (LLMs) are gaining attention for their potential to enhance radiology workflows by addressing challenges such as increasing workloads and staff shortages. However, limited knowledge among radiologists and concerns about their practical implementation and ethical implications present challenges. This study investigates radiologists' perspectives on the use of LLMs, exploring their potential benefits, challenges, and impact on workflows and professional roles. An exploratory, qualitative study was conducted using 12 semi-structured interviews with radiology experts. Data were analyzed to assess participants' awareness, attitudes, and perceived applications of LLMs in radiology. LLMs were identified as promising tools for reducing workloads by streamlining tasks like summarizing clinical histories and generating standardized reports, improving communication and efficiency. Participants expressed openness to LLM integration but noted concerns about their impact on human interaction, ethical standards, and liability. The role of radiologists is expected to evolve with LLM adoption, with a shift toward data stewardship and interprofessional collaboration. Barriers to implementation included limited awareness, regulatory constraints, and outdated infrastructure. The integration of LLMs is hindered by regulatory challenges, outdated infrastructure, and limited awareness among radiologists. Policymakers should establish clear, practical regulations to address liability and ethical concerns while ensuring compliance with privacy standards. Investments in modernizing clinical infrastructure and expanding training programs are critical to enable radiologists to effectively use these tools. By addressing these barriers, LLMs can enhance efficiency, reduce workloads, and improve patient care, while preserving the central role of radiologists in diagnostic and therapeutic processes.

PET-computed tomography (CT) has become essential in sarcoma management, offering precise diagnosis, staging, and response assessment by combining metabolic and anatomic imaging. Its high accuracy in detecting primary, recurrent, and metastatic disease guides personalized treatment strategies and enhances interventional procedures like biopsies and ablations. Advances in novel radiotracers and hybrid imaging modalities further improve diagnostic specificity, especially in complex and pediatric cases. Integrating PET-CT with genomic data and artificial intelligence (AI)-driven tools promises to advance personalized medicine, enabling tailored therapies and better outcomes. As a cornerstone of multidisciplinary sarcoma care, PET-CT continues to transform diagnostic and therapeutic approaches in oncology.