Evaluating large language models as an educational tool for meningioma patients: patient and clinician perspectives.

IF 3.3 2区 医学 Q2 ONCOLOGY
Diana-Coralia Dehelean, Sebastian H Maier, Alev Altay-Langguth, Alexander Nitschmann, Michael Schmeling, Daniel F Fleischmann, Paul Rogowski, Christian Trapp, Stefanie Corradini, Claus Belka, Stephan Schönecker, Sebastian N Marschner
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引用次数: 0

Abstract

Background: The study explores the potential of ChatGPT, an advanced large language model (LLM) by OpenAI, in educating patients about meningioma, a common type of brain tumor. While ChatGPT has generated significant debate regarding its utility and ethics, its growing popularity suggests that patients may increasingly use such tools for medical information. The study specifically examines how patients who have undergone radiation therapy for meningioma perceive the information generated by ChatGPT, integrating both patient feedback and clinical assessment.

Methods: Eight meningioma-related questions on diagnosis, treatment options, and radiation therapy were posed to ChatGPT 4. A questionnaire with these responses and feedback items was developed to assess utility, accuracy, clarity, and alignment with patients' experiences. Nine clinicians first rated each response's relevance, correctness, and completeness on a five-point Likert scale. Subsequently, 28 patients with meningioma completed the questionnaire during their first follow-up visit (three months post-radiation therapy). Finally, the same questions were presented to three other large language models (ChatGPT 4o mini, Gemini Free, Gemini Advanced), and seven blinded clinicians rated each model's responses before selecting the most accurate, eloquent, and comprehensive overall.

Results: The study cohort included 28 meningioma patients, mostly female, with a median age of 60 years. Most patients found the information clear, accurate, and reflective of their experiences, with 60% willing to use ChatGPT for future inquiries. Clinicians rated the relevance and correctness of the information highly, although completeness was rated slightly lower, particularly for questions about specific radiation therapy details and side effects. ChatGPT 4 and its newer version ChatGPT 4o mini received the highest, nearly identical scores among the four LLMs evaluated, while Gemini Free scored the lowest in clinician assessments.

Conclusions: ChatGPT demonstrates potential as a supplementary educational tool for meningioma patients, though some areas may require improvement, particularly in providing comprehensive information. The study highlights the potential for integrating AI in patient education, while also noting the need for clinical oversight to ensure accuracy and completeness.

Trial registration: LMU ethics vote nr.: 23-0742.

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评估大型语言模型作为脑膜瘤患者的教育工具:患者和临床医生的观点。
背景:本研究探讨了ChatGPT (OpenAI开发的一种先进的大语言模型(LLM))在脑膜瘤(一种常见的脑肿瘤)教育患者方面的潜力。虽然ChatGPT在其效用和道德方面引发了重大争论,但它的日益普及表明,患者可能会越来越多地使用此类工具获取医疗信息。该研究专门研究了接受脑膜瘤放射治疗的患者如何感知ChatGPT产生的信息,将患者反馈和临床评估结合起来。方法:向ChatGPT 4提出8个与脑膜瘤有关的诊断、治疗方案和放射治疗问题。开发了一份包含这些回答和反馈项目的问卷,以评估效用、准确性、清晰度和与患者经验的一致性。九名临床医生首先用李克特五分制对每个回答的相关性、正确性和完整性进行评分。随后,28例脑膜瘤患者在第一次随访期间(放疗后3个月)完成问卷调查。最后,同样的问题被呈现给另外三个大型语言模型(ChatGPT 40 mini、Gemini Free、Gemini Advanced),在选出最准确、最雄辩、最全面的模型之前,7位盲法临床医生对每个模型的回答进行了评分。结果:研究队列包括28例脑膜瘤患者,多数为女性,中位年龄60岁。大多数患者认为信息清晰、准确,并反映了他们的经历,60%的患者愿意在未来的咨询中使用ChatGPT。临床医生对信息的相关性和正确性评价很高,尽管完整性评价略低,特别是关于特定放射治疗细节和副作用的问题。ChatGPT 4及其更新版本ChatGPT 40mini在四个llm评估中得分最高,几乎相同,而Gemini Free在临床医生评估中得分最低。结论:ChatGPT显示了作为脑膜瘤患者补充教育工具的潜力,尽管一些领域可能需要改进,特别是在提供全面的信息方面。该研究强调了将人工智能整合到患者教育中的潜力,同时也指出了临床监督的必要性,以确保准确性和完整性。试验报名:LMU伦理投票号码:23-0742。
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
Radiation Oncology
Radiation Oncology ONCOLOGY-RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
CiteScore
6.50
自引率
2.80%
发文量
181
审稿时长
3-6 weeks
期刊介绍: Radiation Oncology encompasses all aspects of research that impacts on the treatment of cancer using radiation. It publishes findings in molecular and cellular radiation biology, radiation physics, radiation technology, and clinical oncology.
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