医学生对医疗保健中大语言模型的认知:一项跨国横断面研究。

IF 1.6 Q2 EDUCATION, SCIENTIFIC DISCIPLINES
Faiza Ejas, Sameer Asim Khan, Amina Mujahid, Fatma AlJoker, Hans Mautong, Geovanny Alvarado-Villa, Abhishek Kashyap, Muhammad Umer Yasir, Kindie Woubshet Nigatu, Nethra Jain, Nandhini Iyer, Aman Sandhu, Shahab Sharafat, Sara Yahya, Mohamd Mahmoud Ghaly, Ibrahim Ibrar, Aakanksha Singh, Harpeet Grewal, Ivan Alfredo Huespe, Priyal Mehta, Zara Arshad, Rahul Kashyap, Faisal A Nawaz
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引用次数: 0

摘要

背景:人工智能(AI)和大型语言模型(llm)是加强医疗保健服务和临床研究的潜在工具。目前,关于医学生对法学硕士的看法的研究很少。本研究旨在探讨法学硕士的观念及其在这一人口统计学中的应用。方法:采用在线调查方法进行横断面研究。它采用谷歌表格设计,于2023年7月至2023年8月发行。目标人群包括来自厄瓜多尔、埃塞俄比亚、印度、毛里求斯、巴基斯坦、阿拉伯联合酋长国(阿联酋)和美国的医科学生。结果:共收集了来自7个国家10所医学院的1180份问卷。阿联酋的回答最多(31.7%),其次是厄瓜多尔(17.6%)和毛里求斯(13.7%)。77.4%的受访者在尝试调查之前已经知道llm,最受欢迎的是ChatGPT。参与者最熟悉法学硕士在研究中的具体使用(46%),最不熟悉的是法学硕士在医疗保健中的使用(37%)。超过一半的参与者(52%)支持在医疗保健领域使用法学硕士,但他们中有相当多的人对法学硕士在这方面的安全性保持中立(37%)或不同意(31%)。结论:虽然大多数医学生都知道法学硕士及其应用,但跨国调查显示医学生在将法学硕士充分应用于医疗实践和临床研究方面存在犹豫。本研究进一步探讨医学生对整合法学硕士的研究方法、伦理考虑和医疗实践的态度和接受程度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Medical Students' Perceptions of Large Language Models in Healthcare: A Multinational Cross-Sectional Study.

Medical Students' Perceptions of Large Language Models in Healthcare: A Multinational Cross-Sectional Study.

Medical Students' Perceptions of Large Language Models in Healthcare: A Multinational Cross-Sectional Study.

Medical Students' Perceptions of Large Language Models in Healthcare: A Multinational Cross-Sectional Study.

Background: Artificial intelligence (AI) and large language models (LLMs), are potential tools for enhancing healthcare delivery and clinical research. Presently, there is a scarcity of research regarding the viewpoints of medical students toward LLMs. This study aims to explore the perceptions of LLMs and their applications among this demographic.

Methods: A cross-sectional study was done using an online survey. It was designed using Google Forms and circulated from July 2023 to August 2023. The target population included medical students from Ecuador, Ethiopia, India, Mauritius, Pakistan, United Arab Emirates (UAE), and the United States (USA).

Results: A total of 1180 responses were collected from 10 medical colleges across 7 countries. The UAE had the largest number of responses (31.7%), followed by Ecuador (17.6%), and Mauritius (13.7%). And 77.4% respondents were already aware about LLMs before attempting the survey, most popularly, ChatGPT. Participants were most familiar with the specific use of LLMs in research (46%), and the lowest familiarity was seen in the use of LLMs in healthcare (37%). More than half of the participants (52%) support the use of LLMs healthcare, yet a significant number of them remained neutral (37%) or disagreed (31%) on LLMs being safe in this context.

Conclusion: While most of the medical students are aware of LLMs and their applications, the multinational survey demonstrated hesitancy among medical students in fully adopting LLMs into healthcare practice and clinical research. This study prompts further exploration of medical students' attitudes and acceptance concerning the integration of LLMs for research methodologies, ethical considerations, and healthcare practice.

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Journal of Medical Education and Curricular Development
Journal of Medical Education and Curricular Development EDUCATION, SCIENTIFIC DISCIPLINES-
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