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Automating Injury Severity Score Calculation Using Large Language Models: A Feasibility Study With Large Language

Sheng-Yu Chan¹, Pang-Chun Liao², Albert Jow³

¹Department of Trauma and Emergency Surgery, Chang Gung University, Chang Gung Memorial Hospital, Taoyuan, Taiwan.

The Journal of Surgical Research

|December 31, 2025

Summary

This summary is machine-generated.

A large language model (LLM) can accurately calculate the Injury Severity Score (ISS) for trauma patients, reducing manual errors. This AI tool shows high reliability and accuracy in trauma scoring.

Keywords:

Artificial intelligence Injury severity score Large language model Trauma

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Area of Science:

Medical informatics
Trauma surgery
Artificial intelligence in healthcare

Background:

The Injury Severity Score (ISS) is vital for trauma assessment but manual calculation is error-prone and time-consuming.
Current methods rely on manual scoring by registrars, leading to potential inaccuracies and delays.

Purpose of the Study:

To evaluate the feasibility of using a large language model (LLM) for automated ISS calculation.
To assess the accuracy and reliability of LLM-assisted trauma scoring compared to manual methods.

Main Methods:

A retrospective study at a level I trauma center using 2022 patient data.
LLM trained with structured prompts on trauma scoring principles.
Validation using 100 cases, comparing LLM-generated ISS with registrar-calculated ISS via Pearson correlation, ICC, and Bland-Altman analysis.

Main Results:

High agreement between LLM ISS and registrar ISS (ICC=0.981).
LLM demonstrated high accuracy (0.91) with minimal mean bias (-0.03) in Bland-Altman analysis.
Consistent performance across different ISS ranges.

Conclusions:

LLM-generated ISS is a reliable and accurate automated method for trauma scoring.
Potential to streamline clinical workflows and reduce human error in trauma assessment.
Future research should focus on real-time integration and application to other scoring systems.