相关实验视频

Updated: Jan 7, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

在AIS评估中基于大型语言模型的人工智能的可靠性:Lenke分类和融合级建议

Cemil Aktan¹, Akın Koşar¹, Melih Ünal¹

¹Department of Orthopedics and Traumatology, Antalya Training and Research Hospital, Antalya 07100, Turkey.

Diagnostics (Basel, Switzerland)

|December 30, 2025

概括

目前的大型语言模型 (LLM) 在青少年异常学脊椎病 (AIS) 手术规划中不可靠. 这些人工智能工具在变形分类和融合级别选择方面与专家外科医生达成不良协议,因此在没有进一步验证的情况下不适合临床使用.

科学领域:

脊柱外科手术脊柱外科手术
人工智能在医学中的应用
放射性评估的放射性评估

背景情况:

准确的青少年异常学脊椎病 (AIS) 分类和融合级规划对于手术成功至关重要.
传统方法依赖于科布角度和伦克系统,需要专家解释.
多模式大语言模型 (LLM) 正在出现用于图像分析,但在骨科决策中缺乏验证.

研究的目的:

评估与专家脊椎外科医生相比,对AIS放射评估的当代多式联络LLM的一致性和可重复性.
为了确定LLMs在Lenke分类和AIS患者的融合水平选择中的临床可靠性.

主要方法:

对125名AIS患者的脊柱放射图 (AP,横向,侧向曲) 的回顾性分析.
由两名专家脊椎外科医生进行独立的分类和融合级别的选择,对参考标准达成共识.
通过使用零射击提示的四个多式LLM对放射图进行分析;评估一致性 (科恩的 κ) 和测试-重试可重复性.

主要成果:

专家外科医生表现出高度一致 (Lenke的κ=0.913,融合的κ=0.879).
所有的LLM都显示出机会级别的可复制性和与专家共识的非常低的一致性 (链接: κ=0.001-0.036;合并: κ=0.003-0.053).
LLM显著更快 (~ 秒 vs ~ 11-12 分钟),但缺乏临床可靠性;一个LLM产生了缺失的输出.

结论:

目前,通用多式联运LLM无法提供可靠的Lenke分类或AIS的融合级规划.
在没有任务特定验证的情况下,LLM解释的不良一致性和内部不一致性阻止它们在外科决策中使用.
需要进一步的研究来开发和验证专门的AI工具,以进行准确的AIS评估.

关键词:

列恩克分类的分类方式青少年的异常学变脊椎病变.人工智能的人工智能是人工智能.深度学习是一种深度学习.多模式的大型语言模型

更多相关视频

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

相关概念视频

Classification of Systems-I

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:

Aggregates Classification

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...