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Large Language Models in Injury Prediction Tools: Simplifying User Interactions and Improving Risk Interpretation.

Vivek Bhaskar Kote1, Koen Flores2, Brian Connolly2

  • 1Southwest Research Institute, San Antonio, TX, USA. vivekbhaskar90@gmail.com.

Annals of Biomedical Engineering
|September 26, 2025
PubMed
Summary
This summary is machine-generated.

Large Language Models (LLMs) enhance finite-element (FE) modeling for injury biomechanics. An LLM tool aids novices in predicting trauma outcomes and understanding complex injury metrics, improving accessibility.

Keywords:
Behind armor blunt traumaChatGPTFinite-element modelingInjury riskLLMsResponse surface model

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Area of Science:

  • Biomechanics
  • Computational Modeling
  • Artificial Intelligence

Background:

  • Finite-element (FE) modeling is crucial for injury biomechanics but requires specialized expertise.
  • Accessibility and usability challenges limit the widespread adoption of complex FE models.
  • Large Language Models (LLMs) present an opportunity to bridge this gap.

Purpose of the Study:

  • To develop and evaluate an LLM-based tool for enhancing FE modeling in injury biomechanics.
  • To guide novice users in selecting appropriate response surface models.
  • To improve the prediction of injury outcomes and communication of results.

Main Methods:

  • Development of an LLM-based tool integrated with FE simulation data.
  • Training response surface models on FE simulation results for Behind Armor Blunt Trauma scenarios.
  • LLM-driven guidance for model selection and injury outcome prediction.
  • Natural language generation for explaining complex injury metrics.

Main Results:

  • The LLM tool successfully guided novice users in selecting response surface models.
  • Accurate prediction of injury outcomes in Behind Armor Blunt Trauma scenarios was achieved.
  • Complex injury metrics were communicated effectively in non-technical language.
  • Enhanced user interaction and understanding of FE modeling were demonstrated.

Conclusions:

  • LLM integration significantly improves the accessibility and usability of FE modeling in injury biomechanics.
  • The developed tool bridges expertise gaps, fostering broader adoption of advanced modeling techniques.
  • LLMs show potential for enhancing decision-making in injury prediction and other engineering fields.