Artificial Intelligence in Radiology: Unlocking New Dimensions of Value.
Authors
Affiliations (26)
Affiliations (26)
- Department of Radiology, Medical Center, University of Freiburg, Faculty of Medicine - University of Freiburg, Freiburg, Germany.
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Hospital Hamburg Eppendorf, Hamburg, Germany.
- Department of Diagnostic and Interventional Radiology, Heinrich Heine University DĂĽsseldorf, DĂĽsseldorf, Germany.
- Institute of Radiology and Nuclear Medicine, University Hospital Schleswig Holstein, Kiel, Germany.
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany.
- Department of Diagnostic and Interventional Radiology, University Hospital of WĂĽrzburg, WĂĽrzburg, Germany.
- University Insitute of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, Minden, Germany.
- Clinic of Diagnostic and Interventional Radiology, University Medical Center Homburg Saarland, Homburg, Germany.
- Department of Diagnostic and Interventional Radiology, University Medical Center Leipzig, Leipzig, Germany.
- TU Dresden, Medizinische Fakultät Carl Gustav Carus, Institut und Poliklinik für Diagnostische und Interventionelle Radiologie, Dresden, Germany.
- Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany.
- German Center of Lung Research, Heidelberg, Germany.
- Department of Diagnostic and Interventional Radiology, University Hospital Giessen, Giessen, Germany.
- Institute of Cardiovascular Imaging; Center for Clinical Radiologic Imaging, University Medical Center Göttingen, Göttingen, Germany.
- Department of Diagnostic and Interventional Radiology, Marburg University Hospital, Fachbereich 20, Philipps Universität Marburg, Marburg, Germany.
- Department of Radiology, TUM Klinikum, Munich, Germany.
- Department of Diagnostic and Interventional Radiology, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany.
- Clinic of Radiology and Nuclear Medicine, University Medical Center Magdeburg, Magdeburg, Germany.
- Department of Radiology and Nuclear Medicine, University Medical Centre Mannheim, Mannheim, Germany.
- Department of Diagnostic and Interventional Radiology, Brandenburg an der Havel University Hospital, Brandenburg Medical School Theodor Fontane, Brandenburg, Germany.
- Institute of Radiology, University Hospital Regensburg, Regensburg, Germany.
- Clinic for Radiology and Nuclear Medicine, University Hospital Frankfurt, Frankfurt, Germany.
- Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, Rostock University Medical Center, Rostock, Germany.
- Department of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany.
- University Clinic and Polyclinic for Radiology, University Hospital Halle, Halle (Saale), Germany.
- Halle MR Imaging Core Facility, Martin Luther University Halle Wittenberg, Halle (Saale), Germany.
Abstract
Artificial intelligence (AI) is emerging as a transformative force in radiology, offering the potential to revolutionize the field by enabling sophisticated analysis of complex radiological data and uncovering previously unknown information in medical images.About a decade after the introduction of clinically applicable AI tools, this article explores the current status, opportunities, and limitations of AI integration in radiological practice. We discuss the growing demand for imaging services, increasing complexity of imaging data, and anticipated workforce shortages. Moreover, the role of large language models, computer vision, and automation in improving diagnostic accuracy, workflow efficiency, and patient communication is highlighted. We also examine the evolving European regulatory framework, including the AI Act, MDR (Medical Device Regulation), and EHDS (European Health Data Space), and their implications for the safe and ethical deployment of AI in clinical settings.Radiology, as a highly digitalized and data-rich specialty, is uniquely positioned to benefit from AI-driven innovations across the entire clinical workflow - from patient scheduling to diagnosis and report generation. Challenges, such as the increasing complexity of imaging data or workforce shortages, further underscore the need for selective, well-validated AI-supported solutions. Despite its promise, current limitations such as data quality, model interpretability, or integration barriers, as well as lack of reimbursement, remain critical challenges.This review underscores the need for thoughtful implementation to fully realize AI's potential as an enabling infrastructure in radiology that makes imaging-based healthcare more efficient, accurate, and accessible. · Artificial intelligence is emerging as a transformative force in diagnostic and interventional radiology.. · This article explores the status, opportunities, and limitations of clinically applicable AI tools.. · The article reviews the evolving European regulatory framework for AI deployment.. · The review highlights the need for interdisciplinary collaboration and well-planned AI implementation based on benefits and evidence.. · Bamberg F, Adam G, Antoch G et al. Artificial Intelligence in Radiology: Unlocking New Dimensions of Value. Rofo 2026; DOI 10.1055/a-2794-9496.