Craniomaxillofacial (CMF) surgery addresses complex challenges, balancing aesthetic and functional restoration. Digital technologies, including advanced imaging, virtual surgical planning, computer-aided design, and 3D printing, have revolutionized this field. These tools improve accuracy and optimize processes across all surgical phases, from diagnosis to postoperative evaluation. CMF's unique demands are met through patient-specific solutions that optimize outcomes. Emerging technologies like artificial intelligence, extended reality, robotics, and bioprinting promise to overcome limitations, driving the future of personalized, technology-driven CMF care.

Breast cancer remains a significant global health issue. Despite advances in detection and treatment, its complexity is driven by genetic, environmental, and structural factors. Computational methods like Finite Element Modeling (FEM) have transformed our understanding of breast cancer risk and progression. Advanced computational approaches in breast cancer research are the focus, with an emphasis on FEM's role in simulating breast tissue mechanics and enhancing precision in therapies such as radiofrequency ablation (RFA). Machine learning (ML), particularly Convolutional Neural Networks (CNNs), has revolutionized imaging modalities like mammograms and MRIs, improving diagnostic accuracy and early detection. AI applications in analyzing histopathological images have advanced tumor classification and grading, offering consistency and reducing inter-observer variability. Explainability tools like Grad-CAM, SHAP, and LIME enhance the transparency of AI-driven models, facilitating their integration into clinical workflows. Integrating FEM and ML represents a paradigm shift in breast cancer management. FEM offers precise modeling of tissue mechanics, while ML excels in predictive analytics and image analysis. Despite challenges such as data variability and limited standardization, synergizing these approaches promises adaptive, personalized care. These computational methods have the potential to redefine diagnostics, optimize treatment, and improve patient outcomes.

Artificial intelligence (AI) can be applied in multiple subspecialties in craniomaxillofacial (CMF) surgeries. This article overviews AI fundamentals focusing on classification, object detection, and segmentation-core tasks used in CMF applications. The article then explores the development and integration of AI in dentoalveolar surgery, implantology, traumatology, oncology, craniofacial surgery, and orthognathic and feminization surgery. It highlights AI-driven advancements in diagnosis, pre-operative planning, intra-operative assistance, post-operative management, and outcome prediction. Finally, the challenges in AI adoption are discussed, including data limitations, algorithm validation, and clinical integration.

Carpal Tunnel Syndrome (CTS) is the most common entrapment neuropathy, frequently presenting with pain, numbness, and muscle weakness due to median nerve compression. However, diagnosing CTS becomes particularly challenging in patients with Congenital Thenar Hypoplasia (CTH), a rare congenital anomaly characterized by underdeveloped thenar muscles. The overlapping symptoms of CTH and CTS, such as thumb weakness, impaired hand function, and thenar muscle atrophy, can obscure the identification of median nerve compression. This review highlights the diagnostic complexities arising from this overlap and evaluates existing clinical, imaging, and electrophysiological assessment methods. While traditional diagnostic tests, including Phalen's and Tinel's signs, exhibit limited sensitivity in CTH patients, advanced imaging modalities like ultrasonography (US), magnetic resonance imaging (MRI), and diffusion tensor imaging (DTI) provide valuable insights into structural abnormalities. Additionally, emerging technologies such as artificial intelligence (AI) enhance diagnostic precision by automating imaging analysis and identifying subtle nerve alterations. Combining clinical history, functional assessments, and advanced imaging, an interdisciplinary approach is critical to differentiate between CTH-related anomalies and CTS accurately. This comprehensive review underscores the need for tailored diagnostic protocols to improve early detection, personalised management, and outcomes for this unique patient population.

Imaging holds a pivotal yet contentious role in the early diagnosis of axial spondyloarthritis. Although MRI has enhanced our ability to detect early inflammatory changes, particularly bone marrow oedema in the sacroiliac joints, the poor specificity of this finding introduces a substantial risk of overdiagnosis. The well intentioned push by rheumatologists towards earlier intervention could inadvertently lead to the misclassification of mechanical or degenerative conditions (eg, osteitis condensans ilii) as inflammatory disease, especially in the absence of structural lesions. Diagnostic uncertainty is further fuelled by anatomical variability, sex differences, and suboptimal imaging protocols. Current strategies-such as quantifying bone marrow oedema and analysing its distribution patterns, and integrating clinical and laboratory data-offer partial guidance for avoiding overdiagnosis but fall short of resolving the core diagnostic dilemma. Emerging imaging technologies, including high-resolution sequences, quantitative MRI, radiomics, and artificial intelligence, could improve diagnostic precision, but these tools remain exploratory. This Viewpoint underscores the need for a shift in imaging approaches, recognising that although timely diagnosis and treatment is essential to prevent long-term structural damage, robust and reliable imaging criteria are also needed. Without such advances, the imaging field risks repeating past missteps seen in other rheumatological conditions.

Information overload in electronic health records requires effective solutions to alleviate clinicians' administrative tasks. Automatically summarizing clinical text has gained significant attention with the rise of large language models. While individual studies show optimism, a structured overview of the research landscape is lacking. This study aims to present the current state of the art on clinical text summarization using large language models, evaluate the level of evidence in existing research and assess the applicability of performance findings in clinical settings. This scoping review complied with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. Literature published between January 1, 2019, and June 18, 2024, was identified from 5 databases: PubMed, Embase, Web of Science, IEEE Xplore, and ACM Digital Library. Studies were excluded if they did not describe transformer-based models, did not focus on clinical text summarization, did not engage with free-text data, were not original research, were nonretrievable, were not peer-reviewed, or were not in English, French, Spanish, or German. Data related to study context and characteristics, scope of research, and evaluation methodologies were systematically collected and analyzed by 3 authors independently. A total of 30 original studies were included in the analysis. All used observational retrospective designs, mainly using real patient data (n=28, 93%). The research landscape demonstrated a narrow research focus, often centered on summarizing radiology reports (n=17, 57%), primarily involving data from the intensive care unit (n=15, 50%) of US-based institutions (n=19, 73%), in English (n=26, 87%). This focus aligned with the frequent reliance on the open-source Medical Information Mart for Intensive Care dataset (n=15, 50%). Summarization methodologies predominantly involved abstractive approaches (n=17, 57%) on single-document inputs (n=4, 13%) with unstructured data (n=13, 43%), yet reporting on methodological details remained inconsistent across studies. Model selection involved both open-source models (n=26, 87%) and proprietary models (n=7, 23%). Evaluation frameworks were highly heterogeneous. All studies conducted internal validation, but external validation (n=2, 7%), failure analysis (n=6, 20%), and patient safety risks analysis (n=1, 3%) were infrequent, and none reported bias assessment. Most studies used both automated metrics and human evaluation (n=16, 53%), while 10 (33%) used only automated metrics, and 4 (13%) only human evaluation. Key barriers hinder the translation of current research into trustworthy, clinically valid applications. Current research remains exploratory and limited in scope, with many applications yet to be explored. Performance assessments often lack reliability, and clinical impact evaluations are insufficient raising concerns about model utility, safety, fairness, and data privacy. Advancing the field requires more robust evaluation frameworks, a broader research scope, and a stronger focus on real-world applicability.

This study explores the application of Retrieval-Augmented Generation (RAG) for question answering in radiology, an area where intelligent systems can significantly impact clinical decision-making. A preliminary experiment tested a naive RAG setup on nice radiology-specific questions with a textbook as the reference source, showing moderate improvements over baseline methods. The paper discusses lessons learned and potential enhancements for RAG in handling radiology knowledge, suggesting pathways for future research in integrating intelligent health systems in medical practice.

Implantation of metallic instrumentation is the mainstay of a variety of orthopaedic and spine surgeries. Postoperatively, imaging of the soft tissues around these implants is commonly required to assess for persistent, recurrent, and/or new pathology (ie, instrumentation loosening, particle disease, infection, neural compression); visualization of these pathologies often requires the superior soft-tissue contrast of magnetic resonance imaging (MRI). As susceptibility artifacts from ferromagnetic implants can result in unacceptable image quality, unique MRI approaches are often necessary to provide accurate imaging. In this text, a comprehensive review is provided on common artifacts encountered in orthopaedic MRI, including comparisons of artifacts from different metallic alloys and common nonpropriety/propriety MR metallic artifact reduction methods. The newest metal-artifact suppression imaging technology and future directions (ie, deep learning/artificial intelligence) in this important field will be considered.

Over the past decade, several studies have explored the potential of magnetic resonance fingerprinting (MRF) for the quantification of brain hemodynamics, oxygenation, and perfusion. Recent advances in simulation models and reconstruction frameworks have also significantly enhanced the accuracy of vascular parameter estimation. This review provides an overview of key vascular MRF studies, emphasizing advancements in geometrical models for vascular simulations, novel sequences, and state-of-the-art reconstruction techniques incorporating machine learning and deep learning algorithms. Both pre-clinical and clinical applications are discussed. Based on these findings, we outline future directions and development areas that need to be addressed to facilitate their clinical translation. EVIDENCE LEVEL: N/A. TECHNICAL EFFICACY: Stage 1.