Vol 27 SJORANM January 2026 Human and Artificial Intelligence in Radiology скачать в хорошем качестве

Vol 27 SJORANM January 2026 Human and Artificial Intelligence in Radiology

Human and Artificial Intelligence in Radiology: Current Status, Evidence, Regulation, and Future Perspectives Zainab Magomedova1,2, Ekaterina S. Pershina1,2, Keivan Daneshvar3, Gerd Nöldge3, Frank Mosler3* 1Department of Radiology of The City Clinical Hospital No. 1 named after NI Pirogov, Moscow, Russian Federation 2Department of Cardiology, Functional and Ultrasound Diagnostics, Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russian Federation 3Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital Bern, University of Bern, Bern, Switzerland Artificial intelligence (AI) has rapidly evolved into a transformative force in radiology, com-plementing 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. Keywords: artificial intelligence, transparency, radiology