Von der Indikationsstellung bis zur Befundung: Potential von Large Language Models im radiologischen Workflow

Zusammenfassung

Hintergrund

Large Language Models (LLMs) bieten angesichts steigender radiologischer Fallzahlen ein vielversprechendes Potenzial zur Optimierung und Unterstützung von Arbeitsabläufen. In dieser Übersicht sollen potenzielle Anwendungsmöglichkeiten im radiologischen Alltag, verbleibende Herausforderungen sowie potenzielle Lösungsansätze diskutiert werden.

Methode

Darstellung der Anwendungsmöglichkeiten und Herausforderungen anhand praxisnaher Beispiele mit konkreten Optimierungsvorschlägen.

Ergebnisse

In nahezu allen Schritten des radiologischen Workflows, die sprachbasierte Prozesse beinhalten, ist der Einsatz von LLM-basierten Assistenzsystemen denkbar. Besonders in der Befunderstellung wurden in den letzten Jahren durch Retrieval-Augmented Generation (RAG) und mehrstufige Argumentationsansätze bedeutende Fortschritte erzielt. Vor einer breiten Implementierung müssen jedoch bleibende Herausforderungen wie Halluzinationen, Reproduzierbarkeit sowie datenschutzrechtliche und ethische Bedenken adressiert werden.

Schlussfolgerung

LLMs haben ein enormes Potenzial in der Radiologie, insbesondere zur Unterstützung sprachbasierter Prozessschritte, wobei technologische Fortschritte wie RAG und cloud-basierte Ansätze die klinische Implementierung näherbringen könnten.

Kernaussagen

• LLMs können mit Technologien wie Retrieval-Augmented Generation (RAG) und mit mehrstufigen Argumentationsansätzen die Befunderstellung und andere sprachbasierte Prozesse in der Radiologie verbessern.

• Vor einer breiten Anwendung müssen Herausforderungen wie Halluzinationen, Reproduzierbarkeit sowie datenschutzrechtliche und ethische Bedenken gelöst werden.

• RAG und cloud-basierte Ansätze könnten helfen, diese Herausforderungen zu überwinden und die klinische Implementierung von LLMs voranzutreiben.

Zitierweise

• Fink A, Rau S, Kästingschäfer K et al. From Referral to Reporting: The Potential of Large Language Models in the Radiological Workflow. Rofo 2025; DOI 10.1055/a-2641-3059

Publication History

Received: 25 March 2025

Accepted after revision: 16 June 2025

Article published online:
16 July 2025

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