比较大型语言模型在具有挑战性的临床病例中的诊断能力。

IF 3 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Frontiers in Artificial Intelligence Pub Date : 2024-08-05 eCollection Date: 2024-01-01 DOI:10.3389/frai.2024.1379297
Maria Palwasha Khan, Eoin Daniel O'Sullivan
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引用次数: 0

摘要

简介:面向消费者的大型语言模型(LLM)的兴起为临床医生提供了即时诊断支持:面向消费者的大型语言模型(LLM)的兴起为临床医生提供了即时诊断支持的机会:比较常见大型语言模型在解决复杂临床病例时的不同性能特点,并评估一种新型工具在对大型语言模型输出进行分级时的效用:使用新开发的评估模型诊断效用的标准,我们根据准确性、可读性、临床可解释性和安全性评估来衡量模型回答病例的能力。在此,我们对三种 LLM 模型--Bing、Chat GPT 和 Gemini- 在《新英格兰医学杂志》病例系列中呈现的各种临床病例进行了比较分析:我们的结果表明,在临床信息相同的情况下,模型的表现各不相同,其中 Gemini 的表现最好。我们的分级工具在观察者之间的变异性较低,证明是对 LLM 临床输出进行分级的可靠工具:这项研究强调了临床场景中模型性能的差异,并突出了在部署之前考虑诊断模型在不同临床场景中性能的重要性。此外,我们还提供了一种评估 LLM 输出的新工具。
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A comparison of the diagnostic ability of large language models in challenging clinical cases.

Introduction: The rise of accessible, consumer facing large language models (LLM) provides an opportunity for immediate diagnostic support for clinicians.

Objectives: To compare the different performance characteristics of common LLMS utility in solving complex clinical cases and assess the utility of a novel tool to grade LLM output.

Methods: Using a newly developed rubric to assess the models' diagnostic utility, we measured to models' ability to answer cases according to accuracy, readability, clinical interpretability, and an assessment of safety. Here we present a comparative analysis of three LLM models-Bing, Chat GPT, and Gemini-across a diverse set of clinical cases as presented in the New England Journal of Medicines case series.

Results: Our results suggest that models performed differently when presented with identical clinical information, with Gemini performing best. Our grading tool had low interobserver variability and proved a reliable tool to grade LLM clinical output.

Conclusion: This research underscores the variation in model performance in clinical scenarios and highlights the importance of considering diagnostic model performance in diverse clinical scenarios prior to deployment. Furthermore, we provide a new tool to assess LLM output.

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来源期刊
CiteScore
6.10
自引率
2.50%
发文量
272
审稿时长
13 weeks
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