From Referral to Reporting: The Potential of Large Language Models in the Radiological Workflow

Abstract

Background

Large language models (LLMs) hold great promise for optimizing and supporting radiology workflows amidst rising workloads. This review examines potential applications in daily radiology practice, as well as remaining challenges and potential solutions.

Method

Presentation of potential applications and challenges, illustrated with practical examples and concrete optimization suggestions.

Results

LLM-based assistance systems have potential applications in almost all language-based process steps of the radiological workflow. Significant progress has been made in areas such as report generation, particularly with retrieval-augmented generation (RAG) and multi-step reasoning approaches. However, challenges related to hallucinations, reproducibility, and data protection, as well as ethical concerns, need to be addressed before widespread implementation.

Conclusion

LLMs have immense potential in radiology, particularly for supporting language-based process steps, with technological advances such as RAG and cloud-based approaches potentially accelerating clinical implementation.

Key Points

• LLMs can optimize reporting and other language-based processes in radiology with technologies such as RAG and multi-step reasoning approaches.

• Challenges such as hallucinations, reproducibility, privacy, and ethical concerns must be addressed before widespread adoption.

• RAG and cloud-based approaches could help overcome these challenges and advance the clinical implementation of LLMs.

Citation Format

• Fink A, Rau S, Kästingschäfer K et al. From Referral to Reporting: The Potential of Large Language Models in the Radiological Workflow. Rofo 2026; 198: 55–63

Keywords

Artificial intelligence - Natural Language Processing - Machine Learning - Deep Learning - Radiology

Publication History

Received: 25 March 2025

Accepted after revision: 16 June 2025

Article published online:
16 July 2025

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