Lumbar Radicular Pain in the Eyes of Artificial Intelligence: Can You 'Imagine' What I 'Feel'?

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2024-10-08 DOI:10.1016/j.wneu.2024.09.075

Mustafa Hüseyin Temel, Yakup Erden, Fatih Bağcıer

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引用次数: 0

Abstract

Objective: Pain is a complex sensory and emotional experience that significantly impacts individuals' well-being. Lumbar radicular pain (LRP) is a prevalent neuropathic pain affecting 9.9% to 25% of the population annually. Accurate identification of pain patterns in LRP is essential for diagnosis and management. Artificial intelligence has potential in health care but faces challenges in reliability and accuracy. This study aimed to investigate the accuracy and consistency of LRP patterns demonstrated by ChatGPT-4o.

Methods: The study was conducted at Üsküdar State Hospital from June 1 to June 30, 2024, utilizing the Generative Pretrained Transformer (GPT), version 4o language model. ChatGPT-4o was prompted to generate and mark LRP patterns for L4, L5, and S1 radiculopathies on an anatomical model. The process was repeated after two weeks to assess consistency. The markings by ChatGPT were compared with those by two experienced specialists using OpenCV for analysis.

Results: ChatGPT's initial and follow-up markings of L4, L5, and S1 radiculopathy pain patterns were statistically significantly different from each other and from the specialists' markings (P < 0.001 for all comparisons).

Conclusions: ChatGPT currently lacks the capacity to accurately and consistently represent LRP patterns. AI tools in health care require further refinement, validation, and regulation to ensure reliability and safety. Future research should involve multiple AI platforms and broader medical conditions to enhance generalizability.

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人工智能眼中的腰椎痛：你能 "想象 "出我的 "感觉 "吗？

目的：疼痛是一种复杂的感官和情绪体验，严重影响个人的身心健康。腰椎痛（LRP）是一种普遍的神经性疼痛，每年影响 9.9% 至 25% 的人口。准确识别腰椎痛的疼痛模式对于诊断和管理至关重要。人工智能在医疗保健领域具有潜力，但在可靠性和准确性方面面临挑战。本研究旨在调查 ChatGPT-4o 显示的 LRP 模式的准确性和一致性：研究于 2024 年 6 月 1 日至 6 月 30 日在于斯库达尔国立医院进行，使用的是 GPT-4o 语言模型。在解剖模型上提示 ChatGPT-4o 生成并标记 L4、L5 和 S1 根神经病的 LRP 模式。两周后重复这一过程以评估一致性。ChatGPT 的标记与两位经验丰富的专家使用 OpenCV 进行的标记进行了比较分析。进行了包括曼-惠特尼 U 检验在内的统计检验：结果：ChatGPT 对 L4、L5 和 S1 根性病变疼痛模式的初始标记和随访标记与专家的标记在统计学上有显著差异（p）：ChatGPT 目前缺乏准确、一致地表现 LRP 模式的能力。医疗保健领域的人工智能工具需要进一步完善、验证和监管，以确保可靠性和安全性。未来的研究应涉及多个人工智能平台和更广泛的医疗条件，以提高普适性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.