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理解医学生对人工智能语言模型的看法、信念和态度:横断面研究。

Understanding Health Care Students' Perceptions, Beliefs, and Attitudes Toward AI-Powered Language Models: Cross-Sectional Study.

机构信息

Universidad Espiritu Santo, Samborondon, Ecuador.

Respiralab Research Group, Guayaquil, Ecuador.

出版信息

JMIR Med Educ. 2024 Aug 13;10:e51757. doi: 10.2196/51757.


DOI:10.2196/51757
PMID:39137029
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11350293/
Abstract

BACKGROUND: ChatGPT was not intended for use in health care, but it has potential benefits that depend on end-user understanding and acceptability, which is where health care students become crucial. There is still a limited amount of research in this area. OBJECTIVE: The primary aim of our study was to assess the frequency of ChatGPT use, the perceived level of knowledge, the perceived risks associated with its use, and the ethical issues, as well as attitudes toward the use of ChatGPT in the context of education in the field of health. In addition, we aimed to examine whether there were differences across groups based on demographic variables. The second part of the study aimed to assess the association between the frequency of use, the level of perceived knowledge, the level of risk perception, and the level of perception of ethics as predictive factors for participants' attitudes toward the use of ChatGPT. METHODS: A cross-sectional survey was conducted from May to June 2023 encompassing students of medicine, nursing, dentistry, nutrition, and laboratory science across the Americas. The study used descriptive analysis, chi-square tests, and ANOVA to assess statistical significance across different categories. The study used several ordinal logistic regression models to analyze the impact of predictive factors (frequency of use, perception of knowledge, perception of risk, and ethics perception scores) on attitude as the dependent variable. The models were adjusted for gender, institution type, major, and country. Stata was used to conduct all the analyses. RESULTS: Of 2661 health care students, 42.99% (n=1144) were unaware of ChatGPT. The median score of knowledge was "minimal" (median 2.00, IQR 1.00-3.00). Most respondents (median 2.61, IQR 2.11-3.11) regarded ChatGPT as neither ethical nor unethical. Most participants (median 3.89, IQR 3.44-4.34) "somewhat agreed" that ChatGPT (1) benefits health care settings, (2) provides trustworthy data, (3) is a helpful tool for clinical and educational medical information access, and (4) makes the work easier. In total, 70% (7/10) of people used it for homework. As the perceived knowledge of ChatGPT increased, there was a stronger tendency with regard to having a favorable attitude toward ChatGPT. Higher ethical consideration perception ratings increased the likelihood of considering ChatGPT as a source of trustworthy health care information (odds ratio [OR] 1.620, 95% CI 1.498-1.752), beneficial in medical issues (OR 1.495, 95% CI 1.452-1.539), and useful for medical literature (OR 1.494, 95% CI 1.426-1.564; P<.001 for all results). CONCLUSIONS: Over 40% of American health care students (1144/2661, 42.99%) were unaware of ChatGPT despite its extensive use in the health field. Our data revealed the positive attitudes toward ChatGPT and the desire to learn more about it. Medical educators must explore how chatbots may be included in undergraduate health care education programs.

摘要

背景:ChatGPT 并非专为医疗保健用途而设计,但它具有潜在的益处,这些益处取决于最终用户的理解和接受程度,而这正是医疗保健学生变得至关重要的地方。在这一领域,相关研究仍然有限。

目的:我们研究的主要目的是评估 ChatGPT 的使用频率、感知到的知识水平、与使用相关的感知风险以及伦理问题,并评估在医疗保健领域的教育背景下使用 ChatGPT 的态度,此外,我们还旨在检验是否存在基于人口统计学变量的群体差异。研究的第二部分旨在评估使用频率、感知知识水平、感知风险水平和感知伦理水平之间的关联,这些因素是预测参与者对使用 ChatGPT 的态度的因素。

方法:本横断面研究于 2023 年 5 月至 6 月进行,涵盖了来自美洲各地的医学生、护理学生、牙科学学生、营养学生和实验室科学学生。研究采用描述性分析、卡方检验和 ANOVA 来评估不同类别之间的统计学意义。研究采用了几个有序逻辑回归模型来分析预测因素(使用频率、感知知识、感知风险和伦理感知得分)对态度作为因变量的影响。模型根据性别、机构类型、专业和国家进行了调整。使用 Stata 进行所有分析。

结果:在 2661 名医疗保健学生中,42.99%(n=1144)不知道 ChatGPT。知识得分的中位数为“最小”(中位数 2.00,IQR 1.00-3.00)。大多数受访者(中位数 2.61,IQR 2.11-3.11)认为 ChatGPT 既不道德也不不道德。大多数参与者(中位数 3.89,IQR 3.44-4.34)“有些同意”,ChatGPT(1)有益于医疗保健环境,(2)提供值得信赖的数据,(3)是获取临床和教育医学信息的有用工具,(4)使工作更容易。总共有 70%(7/10)的人将其用于家庭作业。随着对 ChatGPT 的认知度的提高,对 ChatGPT 的态度也更倾向于正面。更高的伦理考虑因素评分增加了将 ChatGPT 视为可靠医疗保健信息来源的可能性(比值比[OR]1.620,95%置信区间[CI]1.498-1.752),有益于医学问题(OR 1.495,95%CI 1.452-1.539),并有益于医学文献(OR 1.494,95%CI 1.426-1.564;所有结果 P<.001)。

结论:尽管 ChatGPT 在医疗保健领域得到了广泛应用,但超过 40%的美国医疗保健学生(1144/2661,42.99%)不知道 ChatGPT。我们的数据显示了对 ChatGPT 的积极态度以及对更多了解它的渴望。医学教育者必须探索如何将聊天机器人纳入本科医疗保健教育计划。

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引用本文的文献

[1]
Nursing Students' Perceptions of AI-Driven Mental Health Support and Its Relationship with Anxiety, Depression, and Seeking Professional Psychological Help: Transitioning from Traditional Counseling to Digital Support.

Healthcare (Basel). 2025-5-7

[2]
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[3]
Perceptions and future perspectives of medical students on the use of artificial intelligence based chatbots: an exploratory analysis.

Front Med (Lausanne). 2025-1-22

本文引用的文献

[1]
Author Correction: A multinational study on the factors influencing university students' attitudes and usage of ChatGPT.

Sci Rep. 2024-4-9

[2]
"Chatting with ChatGPT": Analyzing the factors influencing users' intention to Use the Open AI's ChatGPT using the UTAUT model.

Heliyon. 2023-10-18

[3]
The Challenges for Regulating Medical Use of ChatGPT and Other Large Language Models.

JAMA. 2023-7-25

[4]
Utility of ChatGPT in Clinical Practice.

J Med Internet Res. 2023-6-28

[5]
Embracing Large Language Models for Medical Applications: Opportunities and Challenges.

Cureus. 2023-5-21

[6]
Investigating the Impact of User Trust on the Adoption and Use of ChatGPT: Survey Analysis.

J Med Internet Res. 2023-6-14

[7]
Student perspectives on the integration of artificial intelligence into healthcare services.

Digit Health. 2023-5-31

[8]
ChatGPT in medicine: an overview of its applications, advantages, limitations, future prospects, and ethical considerations.

Front Artif Intell. 2023-5-4

[9]
User Intentions to Use ChatGPT for Self-Diagnosis and Health-Related Purposes: Cross-sectional Survey Study.

JMIR Hum Factors. 2023-5-17

[10]
Investigating Students' Perceptions towards Artificial Intelligence in Medical Education.

Healthcare (Basel). 2023-5-1

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