多模态大语言模型在激光视力矫正安全指标计算和禁忌证预测中的应用。

Application of multimodal large language models for safety indicator calculation and contraindication prediction in laser vision correction.

作者信息

Choi Joon Yul, Kim Doo Eun, Kim Sung Jin, Choi Hannuy, Yoo Tae Keun

机构信息

Department of Biomedical Engineering, Yonsei University, Wonju, South Korea.

Kim Eye Clinic, Cheongju, Chungcheongbukdo, South Korea.

出版信息

NPJ Digit Med. 2025 Feb 3;8(1):82. doi: 10.1038/s41746-025-01487-4.

DOI:10.1038/s41746-025-01487-4

PMID:39900802

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11790861/

Abstract

This study demonstrates the potential of multimodal large language models in calculating safety indicators and predicting contraindications for laser vision correction. ChatGPT-4 effectively analyzed ocular data, calculated key indicators, generated calculator codes, and outperformed traditional machine learning models and indicators in handling unstructured data and corneal topography. Its modality-independent system enabled efficient and accurate data analysis. Despite longer processing times, ChatGPT-4's performance highlights its potential as a decision-support tool, offering advancements in improving safety.

摘要

本研究证明了多模态大语言模型在计算安全指标和预测激光视力矫正禁忌症方面的潜力。ChatGPT-4有效地分析了眼部数据，计算了关键指标，生成了计算器代码，并且在处理非结构化数据和角膜地形图方面优于传统机器学习模型和指标。其独立于模态的系统实现了高效且准确的数据分析。尽管处理时间较长，但ChatGPT-4的性能凸显了其作为决策支持工具的潜力，为提高安全性带来了进步。