Choi Hongyoon, Lee Dongjoo, Kang Yeon-Koo, Suh Minseok
Department of Nuclear Medicine, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.
Eur J Nucl Med Mol Imaging. 2025 Jun;52(7):2452-2462. doi: 10.1007/s00259-025-07101-9. Epub 2025 Jan 23.
The potential of Large Language Models (LLMs) in enhancing a variety of natural language tasks in clinical fields includes medical imaging reporting. This pilot study examines the efficacy of a retrieval-augmented generation (RAG) LLM system considering zero-shot learning capability of LLMs, integrated with a comprehensive database of PET reading reports, in improving reference to prior reports and decision making.
We developed a custom LLM framework with retrieval capabilities, leveraging a database of over 10 years of PET imaging reports from a single center. The system uses vector space embedding to facilitate similarity-based retrieval. Queries prompt the system to generate context-based answers and identify similar cases or differential diagnoses. From routine clinical PET readings, experienced nuclear medicine physicians evaluated the performance of system in terms of the relevance of queried similar cases and the appropriateness score of suggested potential diagnoses.
The system efficiently organized embedded vectors from PET reports, showing that imaging reports were accurately clustered within the embedded vector space according to the diagnosis or PET study type. Based on this system, a proof-of-concept chatbot was developed and showed the framework's potential in referencing reports of previous similar cases and identifying exemplary cases for various purposes. From routine clinical PET readings, 84.1% of the cases retrieved relevant similar cases, as agreed upon by all three readers. Using the RAG system, the appropriateness score of the suggested potential diagnoses was significantly better than that of the LLM without RAG. Additionally, it demonstrated the capability to offer differential diagnoses, leveraging the vast database to enhance the completeness and precision of generated reports.
The integration of RAG LLM with a large database of PET imaging reports suggests the potential to support clinical practice of nuclear medicine imaging reading by various tasks of AI including finding similar cases and deriving potential diagnoses from them. This study underscores the potential of advanced AI tools in transforming medical imaging reporting practices.
大语言模型（LLMs）在增强临床领域各种自然语言任务方面的潜力包括医学影像报告。本试点研究考察了一种检索增强生成（RAG）大语言模型系统的功效，该系统考虑了大语言模型的零样本学习能力，并与一个全面的PET阅读报告数据库相结合，以改善对先前报告的参考和决策。
我们开发了一个具有检索功能的定制大语言模型框架，利用来自单一中心的超过10年的PET影像报告数据库。该系统使用向量空间嵌入来促进基于相似性的检索。查询促使系统生成基于上下文的答案，并识别相似病例或鉴别诊断。从常规临床PET阅读中，经验丰富的核医学医师根据查询到的相似病例的相关性以及建议的潜在诊断的适宜性评分来评估系统的性能。
该系统有效地组织了来自PET报告的嵌入向量，表明影像报告根据诊断或PET研究类型在嵌入向量空间中被准确聚类。基于该系统，开发了一个概念验证聊天机器人，并展示了该框架在参考先前相似病例报告以及为各种目的识别示例病例方面的潜力。在常规临床PET阅读中，84.1%的病例检索到了相关相似病例，所有三位读者对此表示认同。使用RAG系统，建议的潜在诊断的适宜性评分显著优于没有RAG的大语言模型。此外，它展示了提供鉴别诊断的能力，利用庞大的数据库提高生成报告的完整性和准确性。
RAG大语言模型与大量PET影像报告数据库的整合表明，通过包括查找相似病例并从中推导潜在诊断在内的各种人工智能任务，有可能支持核医学影像阅读的临床实践。本研究强调了先进人工智能工具在改变医学影像报告实践方面的潜力。
