Fink Anna, Rau Stephan, Kästingschäfer Kai, Weiß Jakob, Bamberg Fabian, Russe Maximilian Frederik
Department of Diagnostic and Interventional Radiology, University of Freiburg Faculty of Medicine, Freiburg, Germany.
Rofo. 2025 Jul 16. doi: 10.1055/a-2641-3059.
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.Presentation of potential applications and challenges, illustrated with practical examples and concrete optimization suggestions.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.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. · 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.. · 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.
在工作量不断增加的情况下，大语言模型（LLMs）在优化和支持放射学工作流程方面具有巨大潜力。本综述探讨了其在日常放射学实践中的潜在应用，以及尚存的挑战和潜在解决方案。通过实际例子和具体优化建议展示潜在应用和挑战。基于大语言模型的辅助系统在放射学工作流程中几乎所有基于语言的流程步骤都有潜在应用。在报告生成等领域已取得重大进展，特别是在检索增强生成（RAG）和多步推理方法方面。然而，在广泛实施之前，需要解决与幻觉、可重复性、数据保护以及伦理问题相关的挑战。大语言模型在放射学中具有巨大潜力，特别是在支持基于语言的流程步骤方面，诸如RAG和基于云的方法等技术进步可能会加速临床应用。· 大语言模型可以通过RAG和多步推理方法等技术优化放射学报告及其他基于语言的流程。· 在广泛采用之前，必须解决诸如幻觉、可重复性、隐私和伦理问题等挑战。· RAG和基于云的方法有助于克服这些挑战并推动大语言模型的临床应用。· 芬克A、劳S、卡斯廷施费尔K等。从转诊到报告：大语言模型在放射学工作流程中的潜力。《Rofo》2025年；DOI 10.1055/a - 2641 - 3059 。
