Plain, weight-bearing radiography is the preferred first-line imaging. Dependent on the suspected pathology, further imaging is indicated. In a soft tissue infection, an abscess has to be excluded, for example, with ultrasound. Osteomyelitis has a typical triad including osteolysis, periosteal reaction, and bone destruction in radiography, but signs are often delayed. MRI is the gold standard for diagnosis of osteomyelitis with high intensity in T2-weighted and STIR images and intermediate to decreased reticulated hazy intensity in T1-weighted images. In comparison, bone marrow edema is also bright on the T2-weighted image but the T1-weighted image has a confluent low intensity.

Artificial intelligence (AI) is increasingly employed in radiation protection, encompassing both medical devices and software. These technologies are integrated with AI throughout their manufacturing and application processes. This article underscores the imperative for comprehensive regulation in the utilization of AI. Decisions regarding AI application should not solely rest with manufacturers, medical professionals, or patients. Instead, an overarching "neutral" authority must be engaged to regulate, review, and enforce adherence to established protocols. The authors contend that relying on "self-regulation" within the free market, absent clear guidelines, proves to be inadequately effective and leads to patient's radiation protection safety issues.

Motive: Documentation and administration occupy a substantial part of the working time in interventional radiology. Automated drafting tools may assist with these tasks, but their potential benefit requires evaluation. Method: Three predefined simulated cases of peripherally inserted central catheter (PICC) insertion were used to generate reports with ChatGPT on 4 May 2023, using items from the Radiological Society of North America (RSNA) template. Five iterations per case produced 15 report drafts. Eight radiologists, including six board-certified radiologists and two residents, rated completeness, structure, apparent AI authorship, readiness for use without editing and anticipated time savings. Key results: Raters generally assessed completeness and structure positively and anticipated time savings. They generally disagreed that the drafts were clearly identifiable as AI-generated. Some drafts required editing, particularly in relation to report structure and the handling of complications. Documentation time was not measured, and a controlled comparison with human-written reports was not performed. Conclusions: This small simulation study supports the feasibility of AI-assisted PICC report drafting and describes favourable perceptions among the participating raters. It does not establish actual time savings, equivalent report quality or indistinguishability from human-written reports. Prospective evaluation of drafting time, editing requirements and clinical accuracy is needed before conclusions about routine clinical benefit can be drawn.

Novel artificial intelligence tools have the potential to significantly enhance productivity in medicine, while also maintaining or even improving treatment quality. In this study, we aimed to evaluate the current capability of ChatGPT-4.0 to accurately interpret multimodal musculoskeletal tumor cases. We created 25 cases, each containing images from X-ray, computed tomography, magnetic resonance imaging, or scintigraphy. ChatGPT-4.0 was tasked with classifying each case using a six-option, two-choice question, where both a primary and a secondary diagnosis were allowed. For performance evaluation, human raters also assessed the same cases. When only the primary diagnosis was taken into account, the accuracy of human raters was greater than that of ChatGPT-4.0 by a factor of nearly 2 (87% vs. 44%). However, in a setting that also considered secondary diagnoses, the performance gap shrank substantially (accuracy: 94% vs. 71%). Power analysis relying on Cohen’s w confirmed the adequacy of the sample set size (n: 25). The tested artificial intelligence tool demonstrated lower performance than human raters. Considering factors such as speed, constant availability, and potential future improvements, it appears plausible that artificial intelligence tools could serve as valuable assistance systems for doctors in future clinical settings.

Increased fatty infiltration of the rotator cuff muscles is a primary prognostic factor for poor surgical outcomes of rotator cuff repair surgery. Preoperative fat assessment currently relies on the qualitative Goutallier classification using magnetic resonance imaging (MRI). This method suffers from high observer variability and only assesses a single slice. The aim of this study was to use deep learning to predict quantitative, voxel-wise fat fraction (FF) from standard T1-weighted MRIs. A deep learning-based algorithm was developed for automatic FF prediction using a voxel-wise, five-class system. The network was trained on 75 patients using paired T1-weighted and 2-point Dixon MRI, with rotator cuff muscles segmented in coronal and sagittal planes. It was validated on 24 patients. The proposed algorithm was significantly more accurate than a binary fat classification approach ( p < 0.001). Average whole muscle FF calculation errors (mean ± standard deviation) ranged from − 0.5 ± 2.2% to 2.3 ± 3.9% compared to Dixon MRI measures. Deep learning enabled an accurate, voxel-wise FF quantification using clinical T1-weighted MRIs. This method allows for muscle FF distribution analysis, providing a more comprehensive assessment, that can improve prognosis analysis and optimise treatment planning.

Artificial intelligence (AI) has rapidly evolved into a transformative force in radiology, complementing human intelligence across the entire imaging workflow. Current applications range from image acquisition and reconstruction to automated detection, quantification, triage, and clinical decision support. Evidence to date demonstrates that AI systems can match or exceed human performance in narrowly defined tasks, particularly in pattern recognition and workflow optimization. However, robust prospective validation, demonstration of clinical impact, and proof of generalizability across institutions and populations remain limited. Human intelligence continues to play a central role in contextual interpretation, integration of clinical information, ethical judgment, and responsibility for patient care. Rather than replacing radiologists, AI is increasingly viewed as an augmentative tool that enhances diagnostic accuracy, efficiency, and consistency when appropriately implemented. Regulatory frameworks are evolving in response to these developments. In Europe, the Medical Device Regulation (MDR) and the forthcoming AI Act introduce stricter requirements for transparency, risk classification, post-market surveillance, and human oversight. Comparable regulatory efforts are underway globally, aiming to balance innovation with patient safety, data protection, and accountability. Nonetheless, regulatory heterogeneity and the dynamic nature of adaptive AI systems pose ongoing challenges. Looking ahead, the future of radiology will be shaped by closer human–AI collaboration, increased emphasis on explainability, continuous learning systems under regulatory control, and higher-quality clinical evidence. Education and training of radiologists in AI literacy will be essential. Ultimately, the successful integration of artificial intelligence into radiology will depend not only on technological progress, but also on evidence-based implementation, clear regulation, and sustained human expertise. ----------------------------------------------------------------------------- Contents: 1. Introduction 2. Methods / Sources 3. Radiology Today: Clinical Role and Value Contribution 4. Radiation Protection as Culture (Technology, Behavior, Organization) 5. AI in Radiology: Application Areas and Evidence 6. Generative AI: Support Rather Than Replacement 7. Regulation, Governance, and Quality Assurance 8. Limits of Current Systems: “Common Sense” and Explainability 9. Outlook: Agentive Systems and Division of Labor 10. Practical Checklist: Governance & Safe Implementation 1. Introduction Artificial intelligence (AI) has rapidly evolved into a transformative force in radiology, complementing human intelligence across the entire imaging workflow. Current applications range from image acquisition and reconstruction to automated detection, quantification, triage, and clinical decision support. Evidence to date demonstrates that AI systems can match or exceed human performance in narrowly defined tasks, particularly in pattern recognition and workflow optimization. However, robust prospective validation, demonstration of clinical impact, and proof of generalizability across institutions and populations remain limited. Human intelligence continues to play a central role in contextual interpretation, integration of clinical information, ethical judgment, and responsibility for patient care. Rather than replacing radiologists, AI is increasingly viewed as an augmentative tool that enhances diagnostic accuracy, efficiency, and consistency when appropriately implemented. Regulatory frameworks are evolving in response to these developments. In Europe, the Medical Device Regulation (MDR) and the forthcoming AI Act introduce stricter requirements for transparency, risk classification, post-market surveillance, and human oversight. Comparable regulatory efforts are underway globally, aiming to balance innovation with patient safety, data protection, and accountability. Nonetheless, regulatory heterogeneity and the dynamic nature of adaptive AI systems pose ongoing challenges. Looking ahead, the future of radiology will be shaped by closer human–AI collaboration, increased emphasis on explainability, continuous learning systems under regulatory control, and higher-quality clinical evidence. Education and training of radiologists in AI literacy will be essential. Ultimately, the successful integration of artificial intelligence into radiology will depend not only on technological progress, but also on evidence-based implementation, clear regulation, and sustained human expertise. 2. Methods and Sources The present article is based on a curated, critical analysis of recent high-quality literature addressing the role of artificial intelligence (AI) in radiology. The purpose of this chapter is not to provide a systematic review in the strict methodological sense, but rather to transparently describe the sources used and to analyze how contemporary publications conceptualize, evaluate, and contextualize AI within clinical radiology. Emphasis is placed on methodological rigor, evidence generation, human–AI interaction, and regulatory framing. The selected time frame (2022–2025) captures a period that followed an initial phase of considerable enthusiasm surrounding AI in radiology. During the years preceding this interval, AI was frequently portrayed as a disruptive technology with the potential to fundamentally transform diagnostic imaging, often accompanied by claims of near-human or superhuman performance. More recent publications, however, reflect a noticeable shift toward a more cautious and sober assessment. This phase is characterized by increased attention to real-world performance, unintended consequences of AI deployment, limitations of retrospective evidence, and the growing influence of regulatory requirements (11). Against this background, the present analysis aims to examine how...