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Background <p>Large Language Models (LLMs) like ChatGPT, Llama and Claude are transforming healthcare by interpreting complex text, extracting information, and providing guideline-based support. Radiology, with its high patient volume and digital workflows, is a&#xa0;ideal field for LLM integration.</p> Objective <p>Assessment of the potential of LLMs to enhance efficiency, standardization, and decision support in radiology, while addressing ethical and regulatory challenges.</p> Material and methods <p>Pilot studies at Freiburg and Basel university hospitals evaluated local LLM systems for tasks like prior report summarization and guideline-driven reporting. Integration with Picture Archiving and Communication System (PACS) and Electronic Health Record (EHR) systems was achieved via Digital Imaging and Communications in Medicine (DICOM) and Fast Healthcare Interoperability Resources (FHIR) standards. Metrics included time savings, compliance with the European Union (EU) Artificial Intelligence (AI) Act, and user acceptance.</p> Results <p>LLMs demonstrate significant potential as a&#xa0;support tool for radiologists in clinical practice by reducing reporting times, automating routine tasks, and ensuring consistent, high-quality results. They also support interdisciplinary workflows (e.g., tumor boards) and meet data protection requirements when locally implemented.</p> Discussion <p>Local LLM systems are feasible and beneficial in radiology, enhancing efficiency and diagnostic quality. Future work should refine transparency, expand applications, and ensure LLMs complement medical expertise while adhering to ethical and legal standards.</p>

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Integration von Large Language Models in die Klinik

  • 期刊论文

  • 发表日期：

    2025年3月12日

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  • Philipp Arnold,
  • Maurice Henkel,
  • Fabian Bamberg,
  • Elmar Kotter

摘要

Background

Large Language Models (LLMs) like ChatGPT, Llama and Claude are transforming healthcare by interpreting complex text, extracting information, and providing guideline-based support. Radiology, with its high patient volume and digital workflows, is a ideal field for LLM integration.

Objective

Assessment of the potential of LLMs to enhance efficiency, standardization, and decision support in radiology, while addressing ethical and regulatory challenges.

Material and methods

Pilot studies at Freiburg and Basel university hospitals evaluated local LLM systems for tasks like prior report summarization and guideline-driven reporting. Integration with Picture Archiving and Communication System (PACS) and Electronic Health Record (EHR) systems was achieved via Digital Imaging and Communications in Medicine (DICOM) and Fast Healthcare Interoperability Resources (FHIR) standards. Metrics included time savings, compliance with the European Union (EU) Artificial Intelligence (AI) Act, and user acceptance.

Results

LLMs demonstrate significant potential as a support tool for radiologists in clinical practice by reducing reporting times, automating routine tasks, and ensuring consistent, high-quality results. They also support interdisciplinary workflows (e.g., tumor boards) and meet data protection requirements when locally implemented.

Discussion

Local LLM systems are feasible and beneficial in radiology, enhancing efficiency and diagnostic quality. Future work should refine transparency, expand applications, and ensure LLMs complement medical expertise while adhering to ethical and legal standards.