000282951 001__ 282951
000282951 005__ 20251218153915.0
000282951 0247_ $$2doi$$a10.1007/s00330-025-11745-4
000282951 0247_ $$2pmid$$apmid:40579558
000282951 0247_ $$2ISSN$$a0938-7994
000282951 0247_ $$2ISSN$$a1432-1084
000282951 0247_ $$2ISSN$$a1613-3749
000282951 0247_ $$2ISSN$$a1613-3757
000282951 0247_ $$2ISSN$$a(ISSN
000282951 0247_ $$2ISSN$$aDES
000282951 0247_ $$2ISSN$$aSUPPLEMENTS)
000282951 037__ $$aDZNE-2025-01412
000282951 041__ $$aEnglish
000282951 082__ $$a610
000282951 1001_ $$0P:(DE-2719)9003380$$aWichtmann, Barbara D$$b0$$eFirst author
000282951 245__ $$aLeadership in radiology in the era of technological advancements and artificial intelligence.
000282951 260__ $$aHeidelberg$$bSpringer$$c2026
000282951 3367_ $$2DRIVER$$aarticle
000282951 3367_ $$2DataCite$$aOutput Types/Journal article
000282951 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1766068702_31261$$xReview Article
000282951 3367_ $$2BibTeX$$aARTICLE
000282951 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000282951 3367_ $$00$$2EndNote$$aJournal Article
000282951 520__ $$aRadiology 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.
000282951 536__ $$0G:(DE-HGF)POF4-353$$a353 - Clinical and Health Care Research (POF4-353)$$cPOF4-353$$fPOF IV$$x0
000282951 588__ $$aDataset connected to CrossRef, PubMed, , Journals: pub.dzne.de
000282951 650_7 $$2Other$$aArtificial Intelligence
000282951 650_7 $$2Other$$aGovernance
000282951 650_7 $$2Other$$aLeadership
000282951 650_7 $$2Other$$aRadiology
000282951 650_7 $$2Other$$aWorkflow
000282951 650_2 $$2MeSH$$aArtificial Intelligence
000282951 650_2 $$2MeSH$$aHumans
000282951 650_2 $$2MeSH$$aLeadership
000282951 650_2 $$2MeSH$$aRadiology: organization & administration
000282951 650_2 $$2MeSH$$aRadiology: trends
000282951 7001_ $$0P:(DE-2719)9001705$$aPaech, Daniel$$b1$$udzne
000282951 7001_ $$aPianykh, Oleg S$$b2
000282951 7001_ $$aHuang, Susie Y$$b3
000282951 7001_ $$aSeltzer, Steven E$$b4
000282951 7001_ $$aBrink, James$$b5
000282951 7001_ $$aFennessy, Fiona M$$b6
000282951 773__ $$0PERI:(DE-600)1472718-3$$a10.1007/s00330-025-11745-4$$gVol. 36, no. 1, p. 548 - 552$$n1$$p548 - 552$$tEuropean radiology$$v36$$x0938-7994$$y2026
000282951 8564_ $$uhttps://pub.dzne.de/record/282951/files/DZNE-2025-01412.pdf$$yRestricted
000282951 8564_ $$uhttps://pub.dzne.de/record/282951/files/DZNE-2025-01412.pdf?subformat=pdfa$$xpdfa$$yRestricted
000282951 9101_ $$0I:(DE-588)1065079516$$6P:(DE-2719)9003380$$aDeutsches Zentrum für Neurodegenerative Erkrankungen$$b0$$kDZNE
000282951 9101_ $$0I:(DE-HGF)0$$6P:(DE-2719)9001705$$aExternal Institute$$b1$$kExtern
000282951 9131_ $$0G:(DE-HGF)POF4-353$$1G:(DE-HGF)POF4-350$$2G:(DE-HGF)POF4-300$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lNeurodegenerative Diseases$$vClinical and Health Care Research$$x0
000282951 915__ $$0StatID:(DE-HGF)3002$$2StatID$$aDEAL Springer$$d2024-12-28$$wger
000282951 915__ $$0StatID:(DE-HGF)3002$$2StatID$$aDEAL Springer$$d2024-12-28$$wger
000282951 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2024-12-28
000282951 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2024-12-28
000282951 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2024-12-28
000282951 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2024-12-28
000282951 915__ $$0StatID:(DE-HGF)1110$$2StatID$$aDBCoverage$$bCurrent Contents - Clinical Medicine$$d2024-12-28
000282951 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2024-12-28
000282951 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2024-12-28
000282951 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bEUR RADIOL : 2022$$d2024-12-28
000282951 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search$$d2024-12-28
000282951 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC$$d2024-12-28
000282951 915__ $$0StatID:(DE-HGF)9905$$2StatID$$aIF >= 5$$bEUR RADIOL : 2022$$d2024-12-28
000282951 9201_ $$0I:(DE-2719)5000075$$kAG Radbruch$$lClinical Neuroimaging$$x0
000282951 980__ $$ajournal
000282951 980__ $$aEDITORS
000282951 980__ $$aVDBINPRINT
000282951 980__ $$aI:(DE-2719)5000075
000282951 980__ $$aUNRESTRICTED