Recent advancements in artificial intelligence (AI) have enabled the creation of highly realistic videos demonstrating skin biopsy techniques for dermatology education. This study evaluates the visual realism and clinical accuracy of AI-generated videos, highlighting their potential as educational tools while recognizing significant anatomical inaccuracies that currently limit their clinical reliability. Integration of expert clinical input is essential to optimize the use of such technologies in training environments.

Objectives: Artificial intelligence (AI) is a transformative development in the field of medicine. In the field of musculoskeletal radiology, agentic AI is a technology that could flourish, but currently, the limited evidence base is fragmented and sparse, and we present a scoping review of it. Methods: Parallel searches were conducted in four databases: PubMed, Embase, Scopus, and Web of Science. Search terms included all agentic AI and autonomous AI agents, as well as radiology. All papers underwent screening by two independent reviewers, with conflicts resolved through consensus. Initially, inclusion criteria involved all papers on general radiology, which were later stratified for musculoskeletal radiology and applicable papers to ensure inclusion of all suitable studies. A thematic analysis was undertaken by two independent reviewers. Results: Eleven studies met the inclusion criteria, comprising two MSK (musculoskeletal)-specific and nine general radiology papers applicable to MSK workflows. Four key themes emerged. Agentic decision support was demonstrated across five studies, showing improved diagnostic coordination, pathway navigation, and reduced clinician workload. Workflow optimisation was highlighted in four studies, with agentic systems enhancing administrative efficiency, modality selection, and overall radiology throughput. Image analysis and reconstruction were improved in three studies, with multi-agent systems enabling enhanced image quality and automated interpretation. Finally, four studies addressed conceptual, ethical, and governance considerations, emphasising the need for transparency, safety frameworks, and clinician oversight. Conclusion: Agentic AI shows considerable promise for enhancing MSK radiology through improved decision support, image analysis, and workflow efficiency; however, the current evidence remains limited and largely theoretical.

Background: Scoliosis is a prominent and disabling spinal deformity often presenting in adolescence, causing pain and movement dysfunction. Treatment involves repeated surgical interventions to straighten the spine gradually. Due to the invasive nature and natural disease progression, patients require a deep understanding of the condition for optimal engagement in treatment and associated physiotherapy for rehabilitation. The latest method of patient education is the use of artificial intelligence (AI) to offer tailored information to patients with scoliosis. There are currently no reviews assessing the effects of AI tools in patient education in scoliosis. Methods: Literature searches from PubMed, Embase, Scopus and Web of Science yielded a total of 450 papers after duplicates were excluded. These were independently screened by 2 reviewers with conflicts resolved through consensus, resulting in 5 studies for data extraction. Key themes and information were extracted by two reviewers and grouped by similar trends for analysis. Results: The 5 included studies were conducted between 2023 and 2025, in the USA and Europe. They predominantly assessed the use of AI chatbots such as ChatGPT and Google Bard to deliver content and FAQ-style information. Most studies assessed readability, perceived usefulness and reliability, and reported high accuracy ratings and user enjoyment. All 5 studies highlighted the promise of AI in scoliosis patient education, but underlined the need for clinician oversight to ensure the accuracy of the information provided and tailoring to individuals to maximise results. Conclusion: AI has an emerging and promising role in patient education; however, clinician involvement and integration are paramount to target the relevant information to best benefit patients. Current issues included text which was too complicated for the target audience, a lack of trust in the output and a lack of clinician oversight. Despite this, all 5 studies reported overall positive results for the adoption of AI in patient education in scoliosis.

Background: The symbiotic relationship between industrial advancement and healthcare delivery has fundamentally shaped modern medicine. This comprehensive review article explores the historical and technological trajectory from the First Industrial Revolution (Industry 1.0) to the rapidly emerging Sixth Industrial Revolution (Industry 6.0), meticulously mapping distinct industrial milestones to their concurrent evolutions in clinical practice. Methods: An exhaustive electronic search was conducted across multiple premier academic and clinical databases, including PubMed/MEDLINE, Embase, Scopus, Web of Science, and the Cochrane Library using specific technological enablers, clinical application domains and reported barriers to implementation. Results: While Industry 1.0 through 3.0 focused on mechanization, mass production, and rudimentary automation, resulting in the institutionalization and digitization of medicine, the advent of Industry 4.0 introduced cyber-physical systems and the Medical Internet of Things/Internet of Medical Things. Subsequently, Industry 5.0 emerged to address the depersonalization of hyper-automation by reintegrating human-centricity, ethical governance, and mass personalization. Conclusion: This review posits that the global ecosystem is now transitioning into Industry 6.0, an era characterized by antifragile manufacturing, quantum computing, deep digital twins, and autonomous cognitive systems. By examining specific applications across diagnosis, treatment, screening, monitoring, prevention, and rehabilitation--with a particular focus on hightechnology fields such as orthopaedics--this paper illustrates a paradigm shift from reactive, standardized interventions to proactive, hyper-personalized, and autonomous healthcare delivery models.

Obesity is characterized by excess adiposity. Magnetic resonance imaging (MRI) is used for quantifying and evaluating adiposity by precisely delineating soft tissues. It is widely used for assessing the visceral fat, but its use in evaluating subcutaneous fat (SCF) deposition in the lower body is scarce. This study aims to determine whether body mass index (BMI) predicts regional adiposity around the knee joint. In this retrospective study, 100 patients who had an MRI of the knee were enrolled. Based on the BMI, the patients were categorized into healthy, overweight, and obese groups. Landmark-based representative coronal sections of a proton density (PD) MRI sequence were used, and two experienced musculoskeletal radiologists calculated the medial fat measurement (MFM). The one-way analysis of variance (ANOVA) method was used for statistical analysis. 27, 41, and 32 patients were enrolled in the healthy, overweight, and obese groups, respectively. There was a clinically significant difference between the groups (F-ratio = 7.70, p-value = 0.00079) for the MFM. There was good intra- and interobserver reliability, with kappa coefficients of 0.8 and 0.9, respectively. BMI is a strong predictor of MFMs, indicating a direct relationship with knee regional adiposity. Landmark-based and straightforward measurements allow for reproducible and replicable assessment of SCF deposition.

Magnetic resonance imaging (MRI) is the established gold standard for the staging of primary bone tumors; however, conventional protocols typically use a site-specific model supplemented by ionizing radiation for systemic screening. Recent advancements in whole-body MRI (WB-MRI) have enabled a “marrow-first” diagnostic approach, allowing for the comprehensive assessment of the primary intraosseous lesion and the entire skeleton in a single, radiation-free study. Technical developments, including Dixon fat suppression and diffusion-weighted imaging (DWI), have significantly enhanced the detection of occult skip metastases, which occur in ∼7.5% of high-grade osteosarcomas and 4.5% of Ewing sarcomas. Beyond malignant staging, WB-MRI offers distinct advantages for the longitudinal surveillance of syndromic conditions such as Ollier disease and hereditary multiple exostoses, where it facilitates a shift toward patient-initiated, symptom-led monitoring. Despite these advances, challenges remain, specifically the “chest gap” in pulmonary nodule detection and the potential for false positives from hematopoietic marrow mimics. This review discusses the technical principles, clinical applications, and diagnostic pitfalls of WB-MRI in bone tumors, emphasizing its role in optimizing staging accuracy while minimizing cumulative radiation exposure.