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.

Patients increasingly have direct access to their medical record. Radiology reports are complex and difficult for patients to understand and contextualize. One solution is to use large language models (LLMs) to translate reports into patient-accessible language. Objective This review summarizes the existing literature on using LLMs for the simplification of patient radiology reports. We also propose guidelines for best practices in future studies. A systematic review was performed following PRISMA guidelines. Studies published and indexed using PubMed, Scopus, and Google Scholar up to February 2025 were included. Inclusion criteria comprised of studies that used large language models for simplification of diagnostic or interventional radiology reports for patients and evaluated readability. Exclusion criteria included non-English manuscripts, abstracts, conference presentations, review articles, retracted articles, and studies that did not focus on report simplification. The Mixed Methods Appraisal tool (MMAT) 2018 was used for bias assessment. Given the diversity of results, studies were categorized based on reporting methods, and qualitative and quantitative findings were presented to summarize key insights. A total of 2126 citations were identified and 17 were included in the qualitative analysis. 71% of studies utilized a single LLM, while 29% of studies utilized multiple LLMs. The most prevalent LLMs included ChatGPT, Google Bard/Gemini, Bing Chat, Claude, and Microsoft Copilot. All studies that assessed quantitative readability metrics (n=12) reported improvements. Assessment of simplified reports via qualitative methods demonstrated varied results with physician vs non-physician raters. LLMs demonstrate the potential to enhance the accessibility of radiology reports for patients, but the literature is limited by heterogeneity of inputs, models, and evaluation metrics across existing studies. We propose a set of best practice guidelines to standardize future LLM research.

Fetal diffusion-weighted magnetic resonance imaging (dMRI) represents a promising modality for the assessment of white matter fiber organization, microstructure and development during pregnancy. Over the past two decades, research using this technology has significantly increased, but no consensus has yet been established on how to best implement and standardize the use of fetal dMRI across clinical and research settings. This scoping review aims to synthesize the various methodological approaches for the analysis of fetal dMRI brain data and their applications. We identified a total of 54 relevant articles and analyzed them across five primary domains: (1) datasets, (2) acquisition protocols, (3) image preprocessing/denoising, (4) image processing/modeling, and (5) brain atlas construction. The review of these articles reveals a predominant reliance on Diffusion Tensor Imaging (DTI) (n=37) to study fiber properties, and deterministic tractography approaches to investigate fiber organization (n=23). However, there is an emerging trend towards the adoption of more advanced techniques that address the inherent limitations of fetal dMRI (e.g. maternal and fetal motion, intensity artifacts, fetus's fast and uneven development), particularly through the application of artificial intelligence-based approaches (n=8). In our view, the results suggest that the potential of fetal brain dMRI is hindered by the methodological heterogeneity of the proposed solutions and the lack of publicly available data and tools. Nevertheless, clinical applications demonstrate its utility in studying brain development in both healthy and pathological conditions.

This mini-review explores the transformative potential of artificial intelligence (AI) in improving the diagnosis, management, and long-term care of congenital heart diseases (CHDs). AI offers significant advancements across the spectrum of CHD care, from prenatal screening to postnatal management and long-term monitoring. Using AI algorithms, enhanced fetal echocardiography, and genetic tests improves prenatal diagnosis and risk stratification. Postnatally, AI revolutionizes diagnostic imaging analysis, providing more accurate and efficient identification of CHD subtypes and severity. Compared with traditional methods, advanced signal processing techniques enable a more precise assessment of hemodynamic parameters. AI-driven decision support systems tailor treatment strategies, thereby optimizing therapeutic interventions and predicting patient outcomes with greater accuracy. This personalized approach leads to better clinical outcomes and reduced morbidity. Furthermore, AI-enabled remote monitoring and wearable devices facilitate ongoing surveillance, thereby enabling early detection of complications and provision of prompt interventions. This continuous monitoring is crucial in the immediate postoperative period and throughout the patient's life. Despite the immense potential of AI, challenges remain. These include the need for standardized datasets, the development of transparent and understandable AI algorithms, ethical considerations, and seamless integration into existing clinical workflows. Overcoming these obstacles through collaborative data sharing and responsible implementation will unlock the full potential of AI to improve the lives of patients with CHD, ultimately leading to better patient outcomes and improved quality of life.

Neoadjuvant immunochemotherapy (nICT) has demonstrated significant potential in improving pathological response rates and survival outcomes for patients with locally advanced esophageal squamous cell carcinoma (ESCC). However, substantial interindividual variability in therapeutic outcomes highlights the urgent need for more precise predictive tools to guide clinical decision-making. Traditional biomarkers remain limited in both predictive performance and clinical feasibility. In recent years, the application of artificial intelligence (AI) in medical imaging has expanded rapidly. By incorporating voxel-level feature maps, the combination of radiomics and deep learning enables the extraction of rich textural, morphological, and microstructural features, while autonomously learning high-level abstract representations from clinical CT images, thereby revealing biological heterogeneity that is often imperceptible to conventional assessments. Leveraging these high-dimensional representations, AI models can provide more accurate predictions of nICT response. Future advancements in foundation models, multimodal integration, and dynamic temporal modeling are expected to further enhance the generalizability and clinical applicability of AI. AI-powered medical imaging is poised to support all stages of perioperative management in ESCC, playing a pivotal role in high-risk patient identification, dynamic monitoring of therapeutic response, and individualized treatment adjustment, thereby comprehensively advancing precision nICT.

Single photon emission computed tomography (SPECT) remains the most widely used modality for the assessment of coronary artery disease (CAD) owing to its diagnostic and prognostic value, cost-effectiveness, broad availability, and ability to be performed with exercise testing. However, major cardiology guidelines recommend positron emission tomography (PET) over SPECT when available, largely due to its superior accuracy and ability to provide absolute myocardial blood flow quantification. A key limitation of SPECT is its reliance on relative perfusion imaging, which may overlook diffuse flow reductions, such as those seen in balanced ischemia, diffuse atherosclerosis, and microvascular dysfunction. With the shifting paradigm of CAD toward non-obstructive disease, the need for absolute quantification has become increasingly critical. This review highlights the strengths and limitations of SPECT and explores strategies to preserve its clinical relevance in the PET era. These include the adoption of CZT-SPECT technology for quantification, the use of hybrid systems for attenuation correction and calcium scoring, the adoption of stress-only protocols, the integration of quantitative data and calcium scoring into reporting, and the emerging applications of artificial intelligence (AI) among others.

Multi-modal medical image fusion (MMIF) is increasingly recognized as an essential technique for enhancing diagnostic precision and facilitating effective clinical decision-making within computer-aided diagnosis systems. MMIF combines data from X-ray, MRI, CT, PET, SPECT, and ultrasound to create detailed, clinically useful images of patient anatomy and pathology. These integrated representations significantly advance diagnostic accuracy, lesion detection, and segmentation. This comprehensive review meticulously surveys the evolution, methodologies, algorithms, current advancements, and clinical applications of MMIF. We present a critical comparative analysis of traditional fusion approaches, including pixel-, feature-, and decision-level methods, and delves into recent advancements driven by deep learning, generative models, and transformer-based architectures. A critical comparative analysis is presented between these conventional methods and contemporary techniques, highlighting differences in robustness, computational efficiency, and interpretability. The article addresses extensive clinical applications across oncology, neurology, and cardiology, demonstrating MMIF's vital role in precision medicine through improved patient-specific therapeutic outcomes. Moreover, the review thoroughly investigates the persistent challenges affecting MMIF's broad adoption, including issues related to data privacy, heterogeneity, computational complexity, interpretability of AI-driven algorithms, and integration within clinical workflows. It also identifies significant future research avenues, such as the integration of explainable AI, adoption of privacy-preserving federated learning frameworks, development of real-time fusion systems, and standardization efforts for regulatory compliance. This review organizes key knowledge, outlines challenges, and highlights opportunities, guiding researchers, clinicians, and developers in advancing MMIF for routine clinical use and promoting personalized healthcare. To support further research, we provide a GitHub repository that includes popular multi-modal medical imaging datasets along with recent models in our shared GitHub repository.

Radiography is a field of medicine inherently intertwined with technology. The dependency on technology is very high for obtaining images in ultrasound (US), computed tomography (CT), and magnetic resonance imaging (MRI). Although the reduction in radiation dose is not applicable in US and MRI, advancements in technology have made it possible in CT, with ongoing studies aimed at further optimization. The resolution and diagnostic quality of images obtained through advancements in each modality are steadily improving. Additionally, technological progress has significantly shortened acquisition times for CT and MRI. The use of artificial intelligence (AI), which is becoming increasingly widespread worldwide, has also been incorporated into radiography. This technology can produce more accurate and reproducible results in US examinations. Machine learning offers great potential for improving image quality, creating more distinct and useful images, and even developing new US imaging modalities. Furthermore, AI technologies are increasingly prevalent in CT and MRI for image evaluation, image generation, and enhanced image quality.

ST-elevation myocardial infarction (STEMI) is a major cardiac event that requires rapid reperfusion therapy. The same reperfusion mechanism that minimizes infarct size and mortality may paradoxically exacerbate further cardiac damage-a condition known as reperfusion injury. Oxidative stress, calcium excess, mitochondrial malfunction, and programmed cell death mechanisms make myocardial dysfunction worse. Even with the best revascularization techniques, reperfusion damage still jeopardizes the long-term prognosis and myocardial healing. A thorough narrative review was carried out using some of the most well-known scientific databases, including ScienceDirect, PubMed, and Google Scholar. With an emphasis on pathophysiological causes, clinical manifestations, innovative biomarkers, imaging modalities, artificial intelligence applications, and developing treatment methods related to reperfusion injury, peer-reviewed publications published between 2015 and 2025 were highlighted. The review focuses on the molecular processes that underlie cardiac reperfusion injury, such as reactive oxygen species, calcium dysregulation, opening of the mitochondrial permeability transition pore, and several types of programmed cell death. Clinical syndromes such as myocardial stunning, coronary no-reflow, and intramyocardial hemorrhage are thoroughly studied-all of which lead to negative consequences like heart failure and left ventricular dysfunction. Cardiac magnetic resonance imaging along with coronary angiography and significant biomarkers like N-terminal proBNP and soluble ST2 aid in risk stratification and prognosis. In addition to mechanical techniques like ischemia postconditioning and remote ischemic conditioning, pharmacological treatments are also examined. Despite promising research findings, the majority of therapies have not yet proven consistently effective in extensive clinical studies. Consideration of sex-specific risk factors, medicines that target the mitochondria, tailored therapies, and the use of artificial intelligence for risk assessment and early diagnosis are some potential future avenues. Reperfusion damage continues to be a significant obstacle to the best possible recovery after STEMI, even with improvements in revascularization. The management of STEMI still relies heavily on early reperfusion, although adjuvant medicines that target reperfusion injury specifically are desperately needed. Molecular-targeted approaches, AI-driven risk assessment, and precision medicine advancements have the potential to reduce cardiac damage and enhance long-term outcomes for patients with STEMI.

Technological innovations in robot-assisted ultrasound (RAUS) have remarkably advanced the development of precision and intelligent medical imaging diagnosis. This study aims to use bibliometric methods to systematically analyze the technological evolution and research frontiers in the RAUS field, providing valuable insights for future research. This study used the Web of Science Core Collection database to retrieve English-language research papers and reviews related to RAUS published between 2000 and 2024. Using analytical tools such as R (with the Bibliometrix package), VOSviewer, and CiteSpace, the study conducted a bibliometric analysis from multiple angles, including literature distribution, collaboration networks, and knowledge clustering. The visualization of analysis results comprehensively revealed the hot topics and emerging research frontiers within the RAUS field. The results reveal an exponential growth trend in RAUS research, with China leading in publication output (accounting for 28.51% of total publications), while the USA leads in terms of citation impact and international collaboration networks. Institutions such as Johns Hopkins University and Chinese Academy of Sciences emerge as highly productive core contributors. The research field has formed a multidimensional interdisciplinary landscape encompassing "mathematical sciences-engineering technology-medical health." The focus is on the integration of artificial intelligence (AI) and its clinical application translation. From 2000 to 2014, the development of "mobile robots" laid the cornerstone for further advancements. From 2015 to 2018, research focused on the development of "surgery" and "tumors" for medical applications. From 2019 to 2024, the core focus will be on "medical robots and systems," "artificial intelligence" and "robotic ultrasound," highlighting the transformation of technology into an AI-driven model. This study systematically reviewed the development of RAUS through bibliometric methods, enriching academic understanding of the field and providing valuable guidance for future technological iterations, clinical translation, and global cooperation to ultimately achieve precision medicine and balanced medical resources.