{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T23:00:13Z","timestamp":1785279613375,"version":"3.55.0"},"reference-count":57,"publisher":"Georg Thieme Verlag KG","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Rofo"],"abstract":"<jats:title>Zusammenfassung<\/jats:title>\n                  <jats:p>K\u00fcnstliche Intelligenz (KI) ist in der Radiologie mittlerweile fest etabliert, wobei bislang vor allem aufgabenspezifische Convolutional Neural Networks (CNNs) wie U-Net-Architekturen f\u00fcr Segmentierung, Detektion und Klassifikation eingesetzt werden. Mit dem Aufkommen sogenannter Foundation Models (FMs) vollzieht sich jedoch ein grundlegender Paradigmenwechsel: Diese gro\u00dfskaligen, vortrainierten Modelle sind darauf ausgelegt, sich flexibel an eine Vielzahl radiologischer Aufgaben anzupassen, h\u00e4ufig mit nur minimalem aufgabenspezifischem Fine-Tuning. In der medizinischen Bildgebung basieren FMs typischerweise auf heterogenen, multimodalen Datens\u00e4tzen und erm\u00f6glichen neue Anwendungen wie Zero-Shot-Segmentierung, prompt-basierte Bildexploration und die Integration klinischer Kontextinformationen. Parallel zur akademischen Entwicklung beginnen erste Hersteller, FM-basierte Ans\u00e4tze in kommerzielle radiologische Produkte zu integrieren und klassische, eng spezialisierte\n            Modellarchitekturen schrittweise abzul\u00f6sen. F\u00fcr die klinische Praxis ergeben sich daraus potenzielle Vorteile, etwa in der Reduktion falsch-positiver Befunde, der Automatisierung der Triage in der Notfalldiagnostik oder der verbesserten Detektion komplexer Pathologien. Demgegen\u00fcber stehen jedoch erh\u00f6hte Anforderungen an Rechenressourcen, Kosten sowie bislang ungel\u00f6ste Fragen der Validierung, Erkl\u00e4rbarkeit und regulatorischen Einordnung. Diese \u00dcbersichtsarbeit stellt die konzeptionellen Grundlagen von Foundation Models vor, diskutiert deren technische und \u00f6konomische Implikationen und illustriert anhand klinisch relevanter Anwendungsbeispiele, wie diese neue Modellgeneration die radiologische Bildanalyse nachhaltig ver\u00e4ndern k\u00f6nnte.<\/jats:p>","DOI":"10.1055\/a-2899-1290","type":"journal-article","created":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T22:37:18Z","timestamp":1785278238000},"source":"Crossref","is-referenced-by-count":0,"title":["Grundlagen von Foundation Models in der radiologischen Bildverarbeitung: Chancen, Limitationen und klinische Fallbeispiele"],"prefix":"10.1055","author":[{"given":"Iram","family":"Shahzadi","sequence":"additional","affiliation":[{"name":"Ruhr University Bochum, Germany","place":["Bochum"],"department":["University Institute of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital Minden"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2908-7560","authenticated-orcid":false,"given":"Jan","family":"Borggrefe","sequence":"additional","affiliation":[{"name":"Ruhr University Bochum, Germany","place":["Bochum"],"department":["University Institute of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital Minden"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"194","published-online":{"date-parts":[[2026,7,28]]},"reference":[{"key":"ref1","volume-title":"U-net: Convolutional networks for biomedical image segmentation. 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