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Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

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使用大型语言模型转化知情同意生成:混合方法研究研究

Qiming Shi¹, Katherine Luzuriaga¹, Jeroan J Allison²

¹Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, 55 N Lake Ave, Worcester, MA, 01655, United States, 1 508-856-1952.

JMIR medical informatics

|February 13, 2025

概括

此摘要是机器生成的。

大型语言模型 (LLM) 可以生成更易于阅读和理解的临床试验知情同意表格 (ICF). 这项研究表明,LLM生成的ICF提高了清晰度和可操作性,而不会影响准确性.

关键词:

在这里,我们可以看到AIAIAI.在医疗保健中的AI.在ICF ICF的基础上.在法律上,LLMs.人工智能的人工智能是人工智能.临床试验是指临床试验中的临床试验.医疗信息学健康信息学信息同意表格信息同意表格大型语言模型.可读性可读性

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科学领域:

临床试验管理临床试验管理
医疗保健中的人工智能
医疗信息学医疗信息学

背景情况:

临床试验中的知情同意表格 (ICF) 通常是复杂的,阻碍了参与者的理解和参与.
大型语言模型 (LLM) 的进展为简化ICF创建和改善参与者理解提供了潜在的解决方案.

研究的目的:

评估Mistral 8x22B LLM在生成具有增强可读性,可理解性和可操作性的ICF方面的性能.
评估LLM生成的ICF是否与人类生成的版本相比保持了准确性和完整性.

主要方法:

使用Mistral 8x22B LLM处理了四个临床试验协议,以生成关键的ICF段.
一个由八名评估员组成的多学科团队评估了LLM生成的ICF与人类生成的ICF.
评估标准包括完整性,准确性,可读性,可理解性和可操作性,使用关键信息的可读性,可理解性和可操作性指标.

主要成果:

通过LLM生成的ICF显示了与人类生成的ICF相比的准确性和完整性 (P>10).
该LLM显著提高了可读性 (76.39%对66.67%) 和可理解性 (90.63%对67.19%;P=.02).
通过LLM生成的ICF与人类生成的版本 (0%;P<.001) 相比,实现了完美的可操作性得分 (100%),具有很高的评估器一致性 (ICC=0.83).

结论:

米斯特拉8x22B LLM有效地提高了ICF的可读性,可理解性和可操作性,而不会影响准确性.
在临床试验中,LLM提供了一个可扩展和高效的ICF生成方法,有可能改善参与者的理解和同意.