大型语言模型在伤害预测工具:简化用户交互和改进风险解释
Vivek Bhaskar Kote1, Koen Flores2, Brian Connolly2
1Southwest Research Institute, San Antonio, TX, USA. vivekbhaskar90@gmail.com.
Annals of biomedical engineering
|September 26, 2025
概括
大型语言模型 (LLM) 增强了受伤生物力学有限元素 (FE) 建模. 一个LLM工具帮助新手预测创伤结果和理解复杂的伤害指标,提高可访问性.
科学领域:
- 生物力学 生物力学
- 计算建模 计算建模
- 人工智能的人工智能
背景情况:
- 有限元 (FE) 建模对于伤害生物力学至关重要,但需要专门的专业知识.
- 可访问性和可用性挑战限制了复杂的FE模型的广泛采用.
- 大型语言模型 (LLM) 提供了一个弥合这一差距的机会.
研究的目的:
- 开发和评估一个基于LLM的工具,用于增强受伤生物力学中的FE建模.
- 为了指导初学者在选择适当的响应表面模型.
- 改善伤害结果的预测和结果的沟通.
主要方法:
- 开发一个基于LLM的工具,与FE模拟数据集成.
- 在FE模拟结果上的训练响应表面模型用于背后的盔甲不的创伤场景.
- 基于LLM的指导,用于模型选择和伤害结果预测.
- 自然语言生成用于解释复杂的伤害指标.
主要成果:
- 该LLM工具成功指导初学者在选择响应表面模型.
- 在背后的盔甲形创伤场景中,可以准确预测受伤结果.
- 复杂的伤害指标以非技术语言有效地传达.
- 证明了对FE建模的增强用户交互和理解.
结论:
- 通过LLM集成,可以显著提高FE模型在伤害生物力学中的可访问性和可用性.
- 开发的工具弥合了专业知识的差距,促进了更广泛地采用先进的建模技术.
- 在伤害预测和其他工程领域,LLM显示出增强决策的潜力.
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