评估生成式预训练变压器 (GPT) 在阿尔茨海默病管理中的作用:神经学家和人工智能生成反应的比较研究。

IF 5.8 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Jiaqi Zeng, Xiaoyi Zou, Shirong Li, Yao Tang, Sisi Teng, Huanhuan Li, Changyu Wang, Yuxuan Wu, Luyao Zhang, Yunheng Zhong, Jialin Liu, Siru Liu
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引用次数: 0

摘要

背景:阿尔茨海默病(AD)是一种渐进性神经退行性疾病,给患者、护理人员和社会都带来了挑战。可获取的准确信息对于有效管理阿尔茨海默病至关重要:本研究旨在评估生成式预训练转换器(GPT)有关 AD 患者管理和护理的答案的准确性、可理解性、清晰度和实用性:方法:共确定了 14 个与 AD 的预防、治疗和护理相关的问题,分别用中文和英文向 GPT-3.5 和 GPT-4 提出,并请 4 位神经科医生回答。我们生成了 8 组答案(共 112 个),并将其随机编码在答题纸上。接下来,我们请 5 位神经内科医生和 5 位患者家属分别使用 5 分李克特量表对 112 个回答进行评分。我们使用一套 8 个问题的 5 分李克特量表对回答质量进行评估。为了衡量可理解性和参与者的满意度,我们在同一组 8 个问题中就每个方面专门设置了 3 个问题:截至 2023 年 4 月 10 日,5 位评估者神经科医生和 5 位注意力缺失症患者家属对 112 个回答进行了评分:GPT-3.5: n=28, 25%, responses; GPT-4: n=28, 25%, responses; respondent neurologists:56(50%)个回答。由评估者神经科医生评定的前 5 个(4.5%)回复中有 4 个(80%)是 GPT(GPT-3.5+GPT-4)回复,1 个(20%)是回复者神经科医生的回复。在患者家属评定的前 5 个(4.5%)答复中,除第三个答复外,其余均为 GPT 答复。根据神经科医生的评估,神经科医生生成的回复平均得分为 3.9 分(标准差为 0.7),而 GPT 生成的回复得分明显更高(平均得分为 4.4 分,标准差为 0.6;PConclusions:GPT 可以为患者、患者家属、护理人员、护士和神经科医生提供有关注意力缺失症的患者教育材料。这种能力有助于对注意力缺失症患者进行有效的医疗管理,从而提高患者的治疗效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessing the Role of the Generative Pretrained Transformer (GPT) in Alzheimer's Disease Management: Comparative Study of Neurologist- and Artificial Intelligence-Generated Responses.

Background: Alzheimer's disease (AD) is a progressive neurodegenerative disorder posing challenges to patients, caregivers, and society. Accessible and accurate information is crucial for effective AD management.

Objective: This study aimed to evaluate the accuracy, comprehensibility, clarity, and usefulness of the Generative Pretrained Transformer's (GPT) answers concerning the management and caregiving of patients with AD.

Methods: In total, 14 questions related to the prevention, treatment, and care of AD were identified and posed to GPT-3.5 and GPT-4 in Chinese and English, respectively, and 4 respondent neurologists were asked to answer them. We generated 8 sets of responses (total 112) and randomly coded them in answer sheets. Next, 5 evaluator neurologists and 5 family members of patients were asked to rate the 112 responses using separate 5-point Likert scales. We evaluated the quality of the responses using a set of 8 questions rated on a 5-point Likert scale. To gauge comprehensibility and participant satisfaction, we included 3 questions dedicated to each aspect within the same set of 8 questions.

Results: As of April 10, 2023, the 5 evaluator neurologists and 5 family members of patients with AD rated the 112 responses: GPT-3.5: n=28, 25%, responses; GPT-4: n=28, 25%, responses; respondent neurologists: 56 (50%) responses. The top 5 (4.5%) responses rated by evaluator neurologists had 4 (80%) GPT (GPT-3.5+GPT-4) responses and 1 (20%) respondent neurologist's response. For the top 5 (4.5%) responses rated by patients' family members, all but the third response were GPT responses. Based on the evaluation by neurologists, the neurologist-generated responses achieved a mean score of 3.9 (SD 0.7), while the GPT-generated responses scored significantly higher (mean 4.4, SD 0.6; P<.001). Language and model analyses revealed no significant differences in response quality between the GPT-3.5 and GPT-4 models (GPT-3.5: mean 4.3, SD 0.7; GPT-4: mean 4.4, SD 0.5; P=.51). However, English responses outperformed Chinese responses in terms of comprehensibility (Chinese responses: mean 4.1, SD 0.7; English responses: mean 4.6, SD 0.5; P=.005) and participant satisfaction (Chinese responses: mean 4.2, SD 0.8; English responses: mean 4.5, SD 0.5; P=.04). According to the evaluator neurologists' review, Chinese responses had a mean score of 4.4 (SD 0.6), whereas English responses had a mean score of 4.5 (SD 0.5; P=.002). As for the family members of patients with AD, no significant differences were observed between GPT and neurologists, GPT-3.5 and GPT-4, or Chinese and English responses.

Conclusions: GPT can provide patient education materials on AD for patients, their families and caregivers, nurses, and neurologists. This capability can contribute to the effective health care management of patients with AD, leading to enhanced patient outcomes.

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来源期刊
CiteScore
14.40
自引率
5.40%
发文量
654
审稿时长
1 months
期刊介绍: The Journal of Medical Internet Research (JMIR) is a highly respected publication in the field of health informatics and health services. With a founding date in 1999, JMIR has been a pioneer in the field for over two decades. As a leader in the industry, the journal focuses on digital health, data science, health informatics, and emerging technologies for health, medicine, and biomedical research. It is recognized as a top publication in these disciplines, ranking in the first quartile (Q1) by Impact Factor. Notably, JMIR holds the prestigious position of being ranked #1 on Google Scholar within the "Medical Informatics" discipline.
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