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Artificial Intelligence in radiology: unlocking new dimensions of value = Künstliche Intelligenz in der Radiologie: neue Dimensionen von Nutzen und Mehrwert

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 o...

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Main Authors: Bamberg, Fabian (Author) , Adam, Gerhard (Author) , Antoch, Gerald (Author) , Barkhausen, Jörg (Author) , Bäuerle, Tobias (Author) , Bley, Thorsten (Author) , Borggrefe, Jan (Author) , Bücker, Arno (Author) , Denecke, Timm (Author) , Hoffmann, Ralf-Thorsten (Author) , Kauczor, Hans-Ulrich (Author) , Krombach, Gabriele A. (Author) , Lotz, Joachim (Author) , Mahnken, Andreas H. (Author) , Makowski, Marcus R. (Author) , Maurer, Martin (Author) , Pech, Maciej (Author) , Schönberg, Stefan (Author) , Schreyer, Andreas G. (Author) , Stroszczynski, Christian (Author) , Vogl, Thomas J. (Author) , Weber, Marc-André (Author) , Wielpütz, Mark O. (Author) , Wohlgemuth, Walter A. (Author) , Russe, Maximilian F. (Author) , Steinborn, Carmen (Author) , Kotter, Elmar (Author)
Format: Article (Journal)
Language:English
Published: 2026
In: RöFo

ISSN:1438-9010
DOI:10.1055/a-2794-9496
Online Access:Verlag, kostenfrei, Volltext: https://doi.org/10.1055/a-2794-9496
Verlag, kostenfrei, Volltext: http://www.thieme-connect.de.ezproxy.medma.uni-heidelberg.de/DOI/DOI?10.1055/a-2794-9496
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Author Notes:Authors: Fabian Bamberg, Gerhard Adam, Gerald Antoch, Jörg Barkhausen, Tobias Bäuerle, Thorsten Bley, Jan Borggrefe, Arno Bücker, Timm Denecke, Ralf-Thorsten Hoffmann, Hans-Ulrich Kauczor, Gabriele A. Krombach, Joachim Lotz, Andreas H. Mahnken, Marcus R. Makowski, Martin Maurer, Maciej Pech, Stefan O. Schönberg, Andreas G. Schreyer, Christian Stroszczynski, Thomas J. Vogl, Marc-André Weber, Mark O. Wielpütz, Walter A. Wohlgemuth, Maximilian F. Russe, Carmen Steinborn, Elmar Kotter
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Summary: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.
Item Description:Artikel online veröffentlicht: 16. März 2026
Gesehen am 26.03.2026
Physical Description:Online Resource
ISSN:1438-9010
DOI:10.1055/a-2794-9496

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