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| 001 | 282951 | ||
| 005 | 20260105130837.0 | ||
| 024 | 7 | _ | |a pmc:PMC12711943 |2 pmc |
| 024 | 7 | _ | |a 10.1007/s00330-025-11745-4 |2 doi |
| 024 | 7 | _ | |a pmid:40579558 |2 pmid |
| 024 | 7 | _ | |a 0938-7994 |2 ISSN |
| 024 | 7 | _ | |a 1432-1084 |2 ISSN |
| 024 | 7 | _ | |a 1613-3749 |2 ISSN |
| 024 | 7 | _ | |a 1613-3757 |2 ISSN |
| 024 | 7 | _ | |a (ISSN |2 ISSN |
| 024 | 7 | _ | |a DES |2 ISSN |
| 024 | 7 | _ | |a SUPPLEMENTS) |2 ISSN |
| 037 | _ | _ | |a DZNE-2025-01412 |
| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Wichtmann, Barbara D |0 P:(DE-2719)9003380 |b 0 |e First author |
| 245 | _ | _ | |a Leadership in radiology in the era of technological advancements and artificial intelligence. |
| 260 | _ | _ | |a Heidelberg |c 2026 |b Springer |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1766068702_31261 |2 PUB:(DE-HGF) |x Review Article |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
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| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a Radiology has evolved from the pioneering days of X-ray imaging to a field rich in advanced technologies on the cusp of a transformative future driven by artificial intelligence (AI). As imaging workloads grow in volume and complexity, and economic as well as environmental pressures intensify, visionary leadership is needed to navigate the unprecedented challenges and opportunities ahead. Leveraging its strengths in automation, accuracy and objectivity, AI will profoundly impact all aspects of radiology practice-from workflow management, to imaging, diagnostics, reporting and data-driven analytics-freeing radiologists to focus on value-driven tasks that improve patient care. However, successful AI integration requires strong leadership and robust governance structures to oversee algorithm evaluation, deployment, and ongoing maintenance, steering the transition from static to continuous learning systems. The vision of a 'diagnostic cockpit' that integrates multidimensional data for quantitative precision diagnoses depends on visionary leadership that fosters innovation and interdisciplinary collaboration. Through administrative automation, precision medicine, and predictive analytics, AI can enhance operational efficiency, reduce administrative burden, and optimize resource allocation, leading to substantial cost reductions. Leaders need to understand not only the technical aspects but also the complex human, administrative, and organizational challenges of AI's implementation. Establishing sound governance and organizational frameworks will be essential to ensure ethical compliance and appropriate oversight of AI algorithms. As radiology advances toward this AI-driven future, leaders must cultivate an environment where technology enhances rather than replaces human skills, upholding an unwavering commitment to human-centered care. Their vision will define radiology's pioneering role in AI-enabled healthcare transformation. KEY POINTS: Question Artificial intelligence (AI) will transform radiology, improving workflow efficiency, reducing administrative burden, and optimizing resource allocation to meet imaging workloads' increasing complexity and volume. Findings Strong leadership and governance ensure ethical deployment of AI, steering the transition from static to continuous learning systems while fostering interdisciplinary innovation and collaboration. Clinical relevance Visionary leaders must harness AI to enhance, rather than replace, the role of professionals in radiology, advancing human-centered care while pioneering healthcare transformation. |
| 536 | _ | _ | |a 353 - Clinical and Health Care Research (POF4-353) |0 G:(DE-HGF)POF4-353 |c POF4-353 |f POF IV |x 0 |
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| 650 | _ | 7 | |a Artificial Intelligence |2 Other |
| 650 | _ | 7 | |a Governance |2 Other |
| 650 | _ | 7 | |a Leadership |2 Other |
| 650 | _ | 7 | |a Radiology |2 Other |
| 650 | _ | 7 | |a Workflow |2 Other |
| 650 | _ | 2 | |a Artificial Intelligence |2 MeSH |
| 650 | _ | 2 | |a Humans |2 MeSH |
| 650 | _ | 2 | |a Leadership |2 MeSH |
| 650 | _ | 2 | |a Radiology: organization & administration |2 MeSH |
| 650 | _ | 2 | |a Radiology: trends |2 MeSH |
| 700 | 1 | _ | |a Paech, Daniel |0 P:(DE-2719)9001705 |b 1 |u dzne |
| 700 | 1 | _ | |a Pianykh, Oleg S |b 2 |
| 700 | 1 | _ | |a Huang, Susie Y |b 3 |
| 700 | 1 | _ | |a Seltzer, Steven E |b 4 |
| 700 | 1 | _ | |a Brink, James |b 5 |
| 700 | 1 | _ | |a Fennessy, Fiona M |b 6 |
| 773 | _ | _ | |a 10.1007/s00330-025-11745-4 |g Vol. 36, no. 1, p. 548 - 552 |0 PERI:(DE-600)1472718-3 |n 1 |p 548 - 552 |t European radiology |v 36 |y 2026 |x 0938-7994 |
| 856 | 4 | _ | |y OpenAccess |u https://pub.dzne.de/record/282951/files/DZNE-2025-01412.pdf |
| 856 | 4 | _ | |y OpenAccess |x pdfa |u https://pub.dzne.de/record/282951/files/DZNE-2025-01412.pdf?subformat=pdfa |
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| 910 | 1 | _ | |a Deutsches Zentrum für Neurodegenerative Erkrankungen |0 I:(DE-588)1065079516 |k DZNE |b 0 |6 P:(DE-2719)9003380 |
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| 914 | 1 | _ | |y 2026 |
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| 915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b EUR RADIOL : 2022 |d 2024-12-28 |
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| 915 | _ | _ | |a IF >= 5 |0 StatID:(DE-HGF)9905 |2 StatID |b EUR RADIOL : 2022 |d 2024-12-28 |
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| 920 | 1 | _ | |0 I:(DE-2719)5000075 |k AG Radbruch |l Clinical Neuroimaging |x 0 |
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