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 leading journals and expert groups currently approach AI in radiology, how evidence is generated and reported, and where persistent gaps and inconsistencies remain. The overarching perspective is radiological and clinical, with patient safety, accountability, and feasibility of implementation taking precedence over technological optimism. 2.1 Literature identification and selection strategy The literature corpus consists of twelve peer-reviewed publications published between 2022 and 2025. Sources were selected from internationally recognized journals with high relevance for clinical radiology, medical imaging research, digital medicine, and health technology assessment. These include Nature Medicine, Radiology, The Lancet Digital Health, European Radiology, npj Digital Medicine, Insights into Imaging, The British Journal of Radiology, and Value in Health (1–12). Selection criteria focused on publications that met at least one of the following conditions: (a) presentation of original clinical or reader-based evidence on AI performance in radiology, (b) methodological or reporting frameworks for clinical evaluation of AI systems, (c) health-economic evaluation standards applicable to AI-based interventions, or (d) regulatory and legal analyses with direct relevance to radiological practice. Purely technical machine-learning papers without clinical validation, as well as non–peer-reviewed industry reports, were deliberately excluded. The final selection reflects four thematic clusters: clinical evidence and human–AI interaction (7, 8, 10, 12), methodological and reporting standards (4 – 6), regulatory and governance perspectives (7 – 10, 12), and critical commentaries offering a meta-level appraisal of the current state of AI in radiology (11). This approach allows a balanced view across the AI life cycle, from development and evaluation to implementation and oversight. 2.2 Types of publications and study designs The analyzed literature demonstrates substantial heterogeneity with regard to publication type and study design. Original clinical evidence is predominantly derived from retrospective cohort studies and reader studies, often using enriched or curated datasets. A representative example is the large retrospective screening study evaluating AI as an independent or assisting reader in breast cancer screening, which relies on historical mammography data with long-term follow-up (2). Similarly, real-world validation studies in specific disease contexts, such as multiple sclerosis MRI monitoring, remain largely retrospective and context-specific (3). Reader studies assessing human–AI interaction frequently employ simulated reading environments or controlled experimental designs. While these approaches allow detailed analysis of performance metrics and behavioral effects, they inherently differ from routine clinical conditions (1). Prospective randomized trials remain rare, and when present, are often limited to narrow use cases or specific screening settings. A substantial portion of the literature consists of methodological guidance documents and reporting standards, including frameworks for clinical evaluation (4), early-stage decision support assessment (5), and health-economic reporting (6). These publications are normative in nature and aim to raise the methodological bar for future studies rather than to provide empirical performance data. Regulatory and legal analyses form another important category. These papers interpret evolving regulatory frameworks, particularly within the European context, and translate legal requirements into practical implications for radiologists and healthcare institutions (7, 10, 12). Finally, critical commentaries synthesize existing evidence and explicitly challenge prevailing assumptions about efficiency gains, economic benefits, and the transformative impact of AI in routine radiology (11). 2.3 Conceptualization of artificial intelligence in the literature Across the analyzed publications, AI is consistently conceptualized a
Magomedova et al. (Fri,) studied this question.