Scholarlink

Background <p>The integration of Large Language Models (LLMs) into radiological practice offers promising opportunities to support reporting, workflow optimization, and clinical decision-making.</p> Objective <p>To provide an exemplary demonstration of an LLM’s self-reflection on the use of LLMs in radiology and a&#xa0;critical evaluation of their possibilities and limitations.</p> Materials and methods <p>In this article, an LLM (Claude AI, Version&#xa0;3.5 Sonnet AI Assistant, Anthropic, PBC) reflects on its own potential and limitations within the context of radiological practice. Claude was iteratively employed to analyze and systematically present relevant topics.</p> Results <p>The utilized LLM demonstrates remarkable capabilities in generating structured content and identifying radiological applications. LLMs offer promising support but need to be used responsibly for radiological applications.</p> Conclusion <p>LLMs such as Claude are powerful tools whose effectiveness depends on the user’s ability to critically assess the generated content. Addressing ethical and practical challenges is essential to ensure a&#xa0;balance between technological assistance and medical autonomy. Future developments in generative AI, including potential singularity scenarios, require thoughtful and responsible application to maximize clinical benefits and minimize risks.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Die Anwendung von Large Language Models in der Medizin und im Speziellen in der Radiologie

  • 期刊论文

  • 发表日期：

    2025年3月19日

查看全文
  • Alexander Herold,
  • Christian J. Herold,
  • Elmar Kotter

摘要

Background

The integration of Large Language Models (LLMs) into radiological practice offers promising opportunities to support reporting, workflow optimization, and clinical decision-making.

Objective

To provide an exemplary demonstration of an LLM’s self-reflection on the use of LLMs in radiology and a critical evaluation of their possibilities and limitations.

Materials and methods

In this article, an LLM (Claude AI, Version 3.5 Sonnet AI Assistant, Anthropic, PBC) reflects on its own potential and limitations within the context of radiological practice. Claude was iteratively employed to analyze and systematically present relevant topics.

Results

The utilized LLM demonstrates remarkable capabilities in generating structured content and identifying radiological applications. LLMs offer promising support but need to be used responsibly for radiological applications.

Conclusion

LLMs such as Claude are powerful tools whose effectiveness depends on the user’s ability to critically assess the generated content. Addressing ethical and practical challenges is essential to ensure a balance between technological assistance and medical autonomy. Future developments in generative AI, including potential singularity scenarios, require thoughtful and responsible application to maximize clinical benefits and minimize risks.