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核医学中的生成人工智能和大型语言模型:现状和未来前景

Kenji Hirata¹, Yusuke Matsui², Akira Yamada³

¹Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan. khirata@med.hokudai.ac.jp.

Annals of nuclear medicine

|September 25, 2024

概括

大型语言模型 (LLM) 显示了核医学的前景,增强了PET和SPECT成像. 需要进一步的研究来解决AI集成在这个领域的可靠性和偏见.

科学领域:

核医学是一种核医学.
人工智能的人工智能
医疗成像医学成像

背景情况:

大型语言模型 (LLM) 正在医疗领域迅速发展.
在核医学中,LLM的整合,包括PET和SPECT,仍未得到充分研究.
核医学的近期发展包括新型放射性药物和成像技术.

研究的目的:

探索LLMs在核医学中的潜在应用.
审查LLMs和核医学方面的进展.
确定人工智能在核医学中的挑战和伦理考虑.

主要方法:

在放射学和核医学LLM应用的文献综述.
对核医学成像 (PET,SPECT) 的当前发展进行分析.
检查LLM在报告生成,图像解释和教育方面的能力.

主要成果:

实际上,LLM可能会改善核医学报告的结构,诊断援助和研究促进.
在放射学中,LLM已经用于报告生成,图像解释和医学教育.
关键的挑战包括提高LLM可靠性,可解释性和减少偏见.

结论:

临床医学具有重大潜力,可以彻底改变核医学实践.
解决道德考虑和局限性对于人工智能采用至关重要.
核医学是未来LLM研究和开发的关键领域.

关键词:

教育教育教育教育教育教育.生成性AI是一种人工智能.大型语言模型.核医学是一种核医学.在这里,PET是PET.报告生成报告生成报告结构化报告结构化斯佩克特 (Spectre) 是一个运动场.

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