首页 > 最新文献

JMIR Medical Education最新文献

英文 中文
Exploring HTML5 Package Interactive Content in Supporting Learning Through Self-Paced Massive Open Online Courses on Healthy Aging: Mixed Methods Study. 探索 H5P 互动内容在通过自定进度的健康老龄化 MOOCs 支持学习方面的作用:一项横断面试点研究。
IF 3.2 Q1 EDUCATION, SCIENTIFIC DISCIPLINES Pub Date : 2024-08-22 DOI: 10.2196/45468
Pratiwi Rahadiani, Aria Kekalih, Diantha Soemantri, Desak Gede Budi Krisnamurti
<p><strong>Background: </strong>The rapidly aging population and the growth of geriatric medicine in the field of internal medicine are not supported by sufficient gerontological training in many health care disciplines. There is rising awareness about the education and training needed to adequately prepare health care professionals to address the needs of the older adult population. Massive open online courses (MOOCs) might be the best alternative method of learning delivery in this context. However, the diversity of MOOC participants poses a challenge for MOOC providers to innovate in developing learning content that suits the needs and characters of participants.</p><p><strong>Objective: </strong>The primary outcome of this study was to explore students' perceptions and acceptance of HTML5 package (H5P) interactive content in self-paced MOOCs and its association with students' characteristics and experience in using MOOCs.</p><p><strong>Methods: </strong>This study used a cross-sectional design, combining qualitative and quantitative approaches. Participants, predominantly general practitioners from various regions of Indonesia with diverse educational backgrounds and age groups, completed pretests, engaged with H5P interactive content, and participated in forum discussions and posttests. Data were retrieved from the online questionnaire attached to a selected MOOC course. Students' perceptions and acceptance of H5P interactive content were rated on a 6-point Likert scale from 1 (strongly disagree) to 6 (strongly agree). Data were analyzed using SPSS (IBM Corp) to examine demographics, computer literacy, acceptance, and perceptions of H5P interactive content. Quantitative analysis explored correlations, while qualitative analysis identified recurring themes from open-ended survey responses to determine students' perceptions.</p><p><strong>Results: </strong>In total, 184 MOOC participants agreed to participate in the study. Students demonstrated positive perceptions and a high level of acceptance of integrating H5P interactive content within the self-paced MOOC. Analysis of mean (SD) value across all responses consistently revealed favorable scores (greater than 5), ranging from 5.18 (SD 0.861) to 5.45 (SD 0.659) and 5.28 (SD 0.728) to 5.52 (SD 0.627), respectively. This finding underscores widespread satisfaction and robust acceptance of H5P interactive content. Students found the H5P interactive content more satisfying and fun, easier to understand, more effective, and more helpful in improving learning outcomes than material in the form of common documents and learning videos. There is a significant correlation between computer literacy, students' acceptance, and students' perceptions.</p><p><strong>Conclusions: </strong>Students from various backgrounds showed a high level of acceptance and positive perceptions of leveraging H5P interactive content in the self-paced MOOC. The findings suggest potential new uses of H5P interactive content in
背景:随着人口迅速老龄化以及老年医学在内科领域的发展,许多医疗保健学科都没有开展足够的老年学培训。人们越来越意识到,要使医疗保健专业人员做好充分准备,满足老年人口的需求,就必须开展教育和培训。在这种情况下,大规模开放式在线课程(MOOCs)可能是最好的替代学习方法。然而,MOOC 参与者的多样性给 MOOC 提供者带来了挑战,他们需要创新开发适合参与者需求和特点的学习内容:本研究的主要结果是探讨学生对自定进度 MOOCs 中 HTML5 包(H5P)互动内容的看法和接受程度,及其与学生特点和使用 MOOCs 经验的关联:本研究采用横断面设计,结合了定性和定量方法。参与者主要是来自印度尼西亚不同地区的全科医生,具有不同的教育背景和年龄段,他们完成了前测,参与了 H5P 互动内容,并参加了论坛讨论和后测。数据来自选定的 MOOC 课程所附的在线问卷。学生对 H5P 互动内容的看法和接受程度按 1(非常不同意)到 6(非常同意)的 6 点李克特量表进行评分。我们使用 SPSS 对数据进行了分析,以研究人口统计学、计算机知识、接受度以及对 H5P 互动内容的看法。定量分析探讨了相关性,而定性分析则从开放式调查回复中找出了反复出现的主题,以确定学生的看法:共有 184 名 MOOC 参与者同意参与研究。学生们对在自定进度的 MOOC 中整合 H5P 互动内容表现出积极的看法和高度的认可。对所有回答的平均值(± SD)进行分析,结果一致显示得分良好(大于 5 分),分别为 5.18 ± 0.861 至 5.45 ± 0.659 和 5.28 ± 0.728 至 5.52 ± 0.627。这一结果凸显了 H5P 互动内容的广泛满意度和良好接受度。学生们认为,与普通文档和学习视频相比,H5P 互动内容更令人满意、更有趣、更易于理解、更有效,也更有助于提高学习效果。电脑知识、学生的接受程度和学生的看法之间存在明显的相关性:来自不同背景的学生对在自定进度的 MOOC 中利用 H5P 互动内容表现出了高度的接受度和积极的看法。研究结果表明,H5P 互动内容在 MOOC 中可能会有新的用途,如带有弹出问题的互动视频,以替代同步学习。这项研究强调了量身定制的教育策略在支持医护人员专业发展方面的重要意义:
{"title":"Exploring HTML5 Package Interactive Content in Supporting Learning Through Self-Paced Massive Open Online Courses on Healthy Aging: Mixed Methods Study.","authors":"Pratiwi Rahadiani, Aria Kekalih, Diantha Soemantri, Desak Gede Budi Krisnamurti","doi":"10.2196/45468","DOIUrl":"10.2196/45468","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The rapidly aging population and the growth of geriatric medicine in the field of internal medicine are not supported by sufficient gerontological training in many health care disciplines. There is rising awareness about the education and training needed to adequately prepare health care professionals to address the needs of the older adult population. Massive open online courses (MOOCs) might be the best alternative method of learning delivery in this context. However, the diversity of MOOC participants poses a challenge for MOOC providers to innovate in developing learning content that suits the needs and characters of participants.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The primary outcome of this study was to explore students' perceptions and acceptance of HTML5 package (H5P) interactive content in self-paced MOOCs and its association with students' characteristics and experience in using MOOCs.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This study used a cross-sectional design, combining qualitative and quantitative approaches. Participants, predominantly general practitioners from various regions of Indonesia with diverse educational backgrounds and age groups, completed pretests, engaged with H5P interactive content, and participated in forum discussions and posttests. Data were retrieved from the online questionnaire attached to a selected MOOC course. Students' perceptions and acceptance of H5P interactive content were rated on a 6-point Likert scale from 1 (strongly disagree) to 6 (strongly agree). Data were analyzed using SPSS (IBM Corp) to examine demographics, computer literacy, acceptance, and perceptions of H5P interactive content. Quantitative analysis explored correlations, while qualitative analysis identified recurring themes from open-ended survey responses to determine students' perceptions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;In total, 184 MOOC participants agreed to participate in the study. Students demonstrated positive perceptions and a high level of acceptance of integrating H5P interactive content within the self-paced MOOC. Analysis of mean (SD) value across all responses consistently revealed favorable scores (greater than 5), ranging from 5.18 (SD 0.861) to 5.45 (SD 0.659) and 5.28 (SD 0.728) to 5.52 (SD 0.627), respectively. This finding underscores widespread satisfaction and robust acceptance of H5P interactive content. Students found the H5P interactive content more satisfying and fun, easier to understand, more effective, and more helpful in improving learning outcomes than material in the form of common documents and learning videos. There is a significant correlation between computer literacy, students' acceptance, and students' perceptions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Students from various backgrounds showed a high level of acceptance and positive perceptions of leveraging H5P interactive content in the self-paced MOOC. The findings suggest potential new uses of H5P interactive content in","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11377901/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141761457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Integration of ChatGPT Into a Course for Medical Students: Explorative Study on Teaching Scenarios, Students' Perception, and Applications. 将 ChatGPT 纳入医学生课程:关于教学场景、学生感知和应用的探索性研究。
IF 3.2 Q1 EDUCATION, SCIENTIFIC DISCIPLINES Pub Date : 2024-08-22 DOI: 10.2196/50545
Anita V Thomae, Claudia M Witt, Jürgen Barth
<p><strong>Background: </strong>Text-generating artificial intelligence (AI) such as ChatGPT offers many opportunities and challenges in medical education. Acquiring practical skills necessary for using AI in a clinical context is crucial, especially for medical education.</p><p><strong>Objective: </strong>This explorative study aimed to investigate the feasibility of integrating ChatGPT into teaching units and to evaluate the course and the importance of AI-related competencies for medical students. Since a possible application of ChatGPT in the medical field could be the generation of information for patients, we further investigated how such information is perceived by students in terms of persuasiveness and quality.</p><p><strong>Methods: </strong>ChatGPT was integrated into 3 different teaching units of a blended learning course for medical students. Using a mixed methods approach, quantitative and qualitative data were collected. As baseline data, we assessed students' characteristics, including their openness to digital innovation. The students evaluated the integration of ChatGPT into the course and shared their thoughts regarding the future of text-generating AI in medical education. The course was evaluated based on the Kirkpatrick Model, with satisfaction, learning progress, and applicable knowledge considered as key assessment levels. In ChatGPT-integrating teaching units, students evaluated videos featuring information for patients regarding their persuasiveness on treatment expectations in a self-experience experiment and critically reviewed information for patients written using ChatGPT 3.5 based on different prompts.</p><p><strong>Results: </strong>A total of 52 medical students participated in the study. The comprehensive evaluation of the course revealed elevated levels of satisfaction, learning progress, and applicability specifically in relation to the ChatGPT-integrating teaching units. Furthermore, all evaluation levels demonstrated an association with each other. Higher openness to digital innovation was associated with higher satisfaction and, to a lesser extent, with higher applicability. AI-related competencies in other courses of the medical curriculum were perceived as highly important by medical students. Qualitative analysis highlighted potential use cases of ChatGPT in teaching and learning. In ChatGPT-integrating teaching units, students rated information for patients generated using a basic ChatGPT prompt as "moderate" in terms of comprehensibility, patient safety, and the correct application of communication rules taught during the course. The students' ratings were considerably improved using an extended prompt. The same text, however, showed the smallest increase in treatment expectations when compared with information provided by humans (patient, clinician, and expert) via videos.</p><p><strong>Conclusions: </strong>This study offers valuable insights into integrating the development of AI competencies into a
背景:文本生成人工智能(AI)(如 ChatGPT)为医学教育提供了许多机遇和挑战。掌握在临床环境中使用人工智能所需的实用技能至关重要,尤其是对医学教育而言:这项探索性研究旨在调查将 ChatGPT 整合到教学单元中的可行性,并评估课程以及人工智能相关能力对医学生的重要性。由于 ChatGPT 在医学领域的一个可能应用是为患者生成信息,因此我们进一步调查了学生如何看待此类信息的说服力和质量:方法:将 ChatGPT 整合到医科学生混合学习课程的 3 个不同教学单元中。采用混合方法收集定量和定性数据。作为基线数据,我们评估了学生的特征,包括他们对数字创新的开放程度。学生们对 ChatGPT 与课程的整合进行了评估,并分享了他们对医学教育中文本生成人工智能的未来的看法。课程的评估基于柯克帕特里克模型,满意度、学习进度和适用知识被视为关键的评估水平。在整合了 ChatGPT 的教学单元中,学生们在自我体验实验中评估了为患者提供的关于治疗期望说服力的信息视频,并根据不同的提示批判性地审查了使用 ChatGPT 3.5 编写的为患者提供的信息:共有 52 名医学生参与了研究。对课程的综合评估显示,学生对 ChatGPT 整合教学单元的满意度、学习进度和适用性均有所提高。此外,所有评价水平都显示出相互关联性。对数字创新的开放度越高,满意度就越高,其次是适用性也越高。医学生认为,医学课程其他课程中与人工智能相关的能力非常重要。定性分析强调了 ChatGPT 在教学中的潜在应用案例。在整合了 ChatGPT 的教学单元中,学生对使用基本 ChatGPT 提示生成的病人信息的可理解性、病人安全性以及课程中讲授的交流规则的正确应用方面的评分为 "中等"。使用扩展提示后,学生们的评分明显提高。然而,与人类(患者、临床医生和专家)通过视频提供的信息相比,同样的文本对治疗期望的提高最小:本研究为将人工智能能力培养融入混合式学习课程提供了宝贵的见解。整合 ChatGPT 增强了医学生的学习体验。
{"title":"Integration of ChatGPT Into a Course for Medical Students: Explorative Study on Teaching Scenarios, Students' Perception, and Applications.","authors":"Anita V Thomae, Claudia M Witt, Jürgen Barth","doi":"10.2196/50545","DOIUrl":"10.2196/50545","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Text-generating artificial intelligence (AI) such as ChatGPT offers many opportunities and challenges in medical education. Acquiring practical skills necessary for using AI in a clinical context is crucial, especially for medical education.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This explorative study aimed to investigate the feasibility of integrating ChatGPT into teaching units and to evaluate the course and the importance of AI-related competencies for medical students. Since a possible application of ChatGPT in the medical field could be the generation of information for patients, we further investigated how such information is perceived by students in terms of persuasiveness and quality.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;ChatGPT was integrated into 3 different teaching units of a blended learning course for medical students. Using a mixed methods approach, quantitative and qualitative data were collected. As baseline data, we assessed students' characteristics, including their openness to digital innovation. The students evaluated the integration of ChatGPT into the course and shared their thoughts regarding the future of text-generating AI in medical education. The course was evaluated based on the Kirkpatrick Model, with satisfaction, learning progress, and applicable knowledge considered as key assessment levels. In ChatGPT-integrating teaching units, students evaluated videos featuring information for patients regarding their persuasiveness on treatment expectations in a self-experience experiment and critically reviewed information for patients written using ChatGPT 3.5 based on different prompts.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 52 medical students participated in the study. The comprehensive evaluation of the course revealed elevated levels of satisfaction, learning progress, and applicability specifically in relation to the ChatGPT-integrating teaching units. Furthermore, all evaluation levels demonstrated an association with each other. Higher openness to digital innovation was associated with higher satisfaction and, to a lesser extent, with higher applicability. AI-related competencies in other courses of the medical curriculum were perceived as highly important by medical students. Qualitative analysis highlighted potential use cases of ChatGPT in teaching and learning. In ChatGPT-integrating teaching units, students rated information for patients generated using a basic ChatGPT prompt as \"moderate\" in terms of comprehensibility, patient safety, and the correct application of communication rules taught during the course. The students' ratings were considerably improved using an extended prompt. The same text, however, showed the smallest increase in treatment expectations when compared with information provided by humans (patient, clinician, and expert) via videos.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study offers valuable insights into integrating the development of AI competencies into a ","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11360267/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142037198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Newly Qualified Canadian Nurses' Experiences With Digital Health in the Workplace: Comparative Qualitative Analysis. 加拿大新入职护士对工作场所数字健康的体验:比较定性分析。
IF 3.2 Q1 EDUCATION, SCIENTIFIC DISCIPLINES Pub Date : 2024-08-19 DOI: 10.2196/53258
Manal Kleib, Antonia Arnaert, Lynn M Nagle, Rebecca Sugars, Daniel da Costa

Background: Clinical practice settings have increasingly become dependent on the use of digital or eHealth technologies such as electronic health records. It is vitally important to support nurses in adapting to digitalized health care systems; however, little is known about nursing graduates' experiences as they transition to the workplace.

Objective: This study aims to (1) describe newly qualified nurses' experiences with digital health in the workplace, and (2) identify strategies that could help support new graduates' transition and practice with digital health.

Methods: An exploratory descriptive qualitative design was used. A total of 14 nurses from Eastern and Western Canada participated in semistructured interviews and data were analyzed using inductive content analysis.

Results: Three themes were identified: (1) experiences before becoming a registered nurse, (2) experiences upon joining the workplace, and (3) suggestions for bridging the gap in transition to digital health practice. Findings revealed more similarities than differences between participants with respect to gaps in digital health education, technology-related challenges, and their influence on nursing practice.

Conclusions: Digital health is the foundation of contemporary health care; therefore, comprehensive education during nursing school and throughout professional nursing practice, as well as organizational support and policy, are critical pillars. Health systems investing in digital health technologies must create supportive work environments for nurses to thrive in technologically rich environments and increase their capacity to deliver the digital health future.

背景:临床实践环境越来越依赖于使用电子健康记录等数字化或电子医疗技术。支持护士适应数字化医疗系统至关重要;然而,人们对护理专业毕业生过渡到工作场所的经历知之甚少:本研究旨在:(1)描述新近获得资格的护士在工作场所使用数字医疗的经验;(2)确定有助于支持新毕业生过渡和使用数字医疗的策略:采用探索性描述定性设计。共有来自加拿大东部和西部的 14 名护士参加了半结构式访谈,并使用归纳内容分析法对数据进行了分析:结果:确定了三个主题:(结果:确定了三个主题:(1)成为注册护士之前的经历;(2)加入工作场所后的经历;(3)关于缩小向数字医疗实践过渡的差距的建议。研究结果表明,在数字健康教育的差距、与技术相关的挑战及其对护理实践的影响方面,参与者之间的相似之处多于不同之处:数字健康是当代医疗保健的基础;因此,护理学校和整个专业护理实践过程中的全面教育以及组织支持和政策是至关重要的支柱。投资数字医疗技术的医疗系统必须为护士创造有利的工作环境,使其在技术丰富的环境中茁壮成长,并提高其实现数字医疗未来的能力。
{"title":"Newly Qualified Canadian Nurses' Experiences With Digital Health in the Workplace: Comparative Qualitative Analysis.","authors":"Manal Kleib, Antonia Arnaert, Lynn M Nagle, Rebecca Sugars, Daniel da Costa","doi":"10.2196/53258","DOIUrl":"10.2196/53258","url":null,"abstract":"<p><strong>Background: </strong>Clinical practice settings have increasingly become dependent on the use of digital or eHealth technologies such as electronic health records. It is vitally important to support nurses in adapting to digitalized health care systems; however, little is known about nursing graduates' experiences as they transition to the workplace.</p><p><strong>Objective: </strong>This study aims to (1) describe newly qualified nurses' experiences with digital health in the workplace, and (2) identify strategies that could help support new graduates' transition and practice with digital health.</p><p><strong>Methods: </strong>An exploratory descriptive qualitative design was used. A total of 14 nurses from Eastern and Western Canada participated in semistructured interviews and data were analyzed using inductive content analysis.</p><p><strong>Results: </strong>Three themes were identified: (1) experiences before becoming a registered nurse, (2) experiences upon joining the workplace, and (3) suggestions for bridging the gap in transition to digital health practice. Findings revealed more similarities than differences between participants with respect to gaps in digital health education, technology-related challenges, and their influence on nursing practice.</p><p><strong>Conclusions: </strong>Digital health is the foundation of contemporary health care; therefore, comprehensive education during nursing school and throughout professional nursing practice, as well as organizational support and policy, are critical pillars. Health systems investing in digital health technologies must create supportive work environments for nurses to thrive in technologically rich environments and increase their capacity to deliver the digital health future.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11369539/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142005452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Language Model-Powered Simulated Patient With Automated Feedback for History Taking: Prospective Study. 语言模型驱动的模拟病人,自动反馈病史采集:前瞻性研究
IF 3.2 Q1 EDUCATION, SCIENTIFIC DISCIPLINES Pub Date : 2024-08-16 DOI: 10.2196/59213
Friederike Holderried, Christian Stegemann-Philipps, Anne Herrmann-Werner, Teresa Festl-Wietek, Martin Holderried, Carsten Eickhoff, Moritz Mahling

Background: Although history taking is fundamental for diagnosing medical conditions, teaching and providing feedback on the skill can be challenging due to resource constraints. Virtual simulated patients and web-based chatbots have thus emerged as educational tools, with recent advancements in artificial intelligence (AI) such as large language models (LLMs) enhancing their realism and potential to provide feedback.

Objective: In our study, we aimed to evaluate the effectiveness of a Generative Pretrained Transformer (GPT) 4 model to provide structured feedback on medical students' performance in history taking with a simulated patient.

Methods: We conducted a prospective study involving medical students performing history taking with a GPT-powered chatbot. To that end, we designed a chatbot to simulate patients' responses and provide immediate feedback on the comprehensiveness of the students' history taking. Students' interactions with the chatbot were analyzed, and feedback from the chatbot was compared with feedback from a human rater. We measured interrater reliability and performed a descriptive analysis to assess the quality of feedback.

Results: Most of the study's participants were in their third year of medical school. A total of 1894 question-answer pairs from 106 conversations were included in our analysis. GPT-4's role-play and responses were medically plausible in more than 99% of cases. Interrater reliability between GPT-4 and the human rater showed "almost perfect" agreement (Cohen κ=0.832). Less agreement (κ<0.6) detected for 8 out of 45 feedback categories highlighted topics about which the model's assessments were overly specific or diverged from human judgement.

Conclusions: The GPT model was effective in providing structured feedback on history-taking dialogs provided by medical students. Although we unraveled some limitations regarding the specificity of feedback for certain feedback categories, the overall high agreement with human raters suggests that LLMs can be a valuable tool for medical education. Our findings, thus, advocate the careful integration of AI-driven feedback mechanisms in medical training and highlight important aspects when LLMs are used in that context.

背景:虽然病史采集是诊断病情的基础,但由于资源限制,教授病史采集技能并提供反馈可能具有挑战性。因此,虚拟模拟病人和基于网络的聊天机器人已成为教育工具,最近人工智能(AI)的进步,如大型语言模型(LLM),增强了它们的真实性和提供反馈的潜力:在我们的研究中,我们旨在评估生成式预训练转换器(GPT)4 模型为医科学生在模拟病人病史采集中的表现提供结构化反馈的有效性:我们开展了一项前瞻性研究,让医科学生使用由 GPT 驱动的聊天机器人进行病史采集。为此,我们设计了一个聊天机器人来模拟病人的反应,并就学生病史采集的全面性提供即时反馈。我们对学生与聊天机器人的互动进行了分析,并将聊天机器人的反馈与人类评分员的反馈进行了比较。我们测量了评分者之间的可靠性,并进行了描述性分析,以评估反馈的质量:研究的大部分参与者都是医学院三年级的学生。我们的分析共包括 106 次对话中的 1894 对问答。在 99% 以上的案例中,GPT-4 的角色扮演和回答在医学上是可信的。GPT-4 与人类测评者之间的互测可靠性显示出 "几乎完美 "的一致性(Cohen κ=0.832)。一致性较低(κ结论:GPT 模型能有效地对医学生提供的病史采集对话进行结构化反馈。虽然我们发现了某些反馈类别的反馈特异性存在一些局限性,但与人类评分者的总体高度一致表明,LLM 可以成为医学教育的一个有价值的工具。因此,我们的研究结果提倡在医学培训中谨慎整合人工智能驱动的反馈机制,并强调了在此背景下使用 LLM 的重要方面。
{"title":"A Language Model-Powered Simulated Patient With Automated Feedback for History Taking: Prospective Study.","authors":"Friederike Holderried, Christian Stegemann-Philipps, Anne Herrmann-Werner, Teresa Festl-Wietek, Martin Holderried, Carsten Eickhoff, Moritz Mahling","doi":"10.2196/59213","DOIUrl":"10.2196/59213","url":null,"abstract":"<p><strong>Background: </strong>Although history taking is fundamental for diagnosing medical conditions, teaching and providing feedback on the skill can be challenging due to resource constraints. Virtual simulated patients and web-based chatbots have thus emerged as educational tools, with recent advancements in artificial intelligence (AI) such as large language models (LLMs) enhancing their realism and potential to provide feedback.</p><p><strong>Objective: </strong>In our study, we aimed to evaluate the effectiveness of a Generative Pretrained Transformer (GPT) 4 model to provide structured feedback on medical students' performance in history taking with a simulated patient.</p><p><strong>Methods: </strong>We conducted a prospective study involving medical students performing history taking with a GPT-powered chatbot. To that end, we designed a chatbot to simulate patients' responses and provide immediate feedback on the comprehensiveness of the students' history taking. Students' interactions with the chatbot were analyzed, and feedback from the chatbot was compared with feedback from a human rater. We measured interrater reliability and performed a descriptive analysis to assess the quality of feedback.</p><p><strong>Results: </strong>Most of the study's participants were in their third year of medical school. A total of 1894 question-answer pairs from 106 conversations were included in our analysis. GPT-4's role-play and responses were medically plausible in more than 99% of cases. Interrater reliability between GPT-4 and the human rater showed \"almost perfect\" agreement (Cohen κ=0.832). Less agreement (κ<0.6) detected for 8 out of 45 feedback categories highlighted topics about which the model's assessments were overly specific or diverged from human judgement.</p><p><strong>Conclusions: </strong>The GPT model was effective in providing structured feedback on history-taking dialogs provided by medical students. Although we unraveled some limitations regarding the specificity of feedback for certain feedback categories, the overall high agreement with human raters suggests that LLMs can be a valuable tool for medical education. Our findings, thus, advocate the careful integration of AI-driven feedback mechanisms in medical training and highlight important aspects when LLMs are used in that context.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11364946/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141989103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Reforming China's Secondary Vocational Medical Education: Adapting to the Challenges and Opportunities of the AI Era. 中国中等职业医学教育改革:适应人工智能时代的挑战与机遇》。
IF 3.2 Q1 EDUCATION, SCIENTIFIC DISCIPLINES Pub Date : 2024-08-15 DOI: 10.2196/48594
Wenting Tong, Xiaowen Zhang, Haiping Zeng, Jianping Pan, Chao Gong, Hui Zhang

Unlabelled: China's secondary vocational medical education is essential for training primary health care personnel and enhancing public health responses. This education system currently faces challenges, primarily due to its emphasis on knowledge acquisition that overshadows the development and application of skills, especially in the context of emerging artificial intelligence (AI) technologies. This article delves into the impact of AI on medical practices and uses this analysis to suggest reforms for the vocational medical education system in China. AI is found to significantly enhance diagnostic capabilities, therapeutic decision-making, and patient management. However, it also brings about concerns such as potential job losses and necessitates the adaptation of medical professionals to new technologies. Proposed reforms include a greater focus on critical thinking, hands-on experiences, skill development, medical ethics, and integrating humanities and AI into the curriculum. These reforms require ongoing evaluation and sustained research to effectively prepare medical students for future challenges in the field.

无标签:中国的中等职业医学教育对于培养初级卫生保健人员和加强公共卫生应对措施至关重要。目前,这一教育体系面临着挑战,主要是由于其重视知识的学习,而忽视了技能的培养和应用,尤其是在新兴的人工智能(AI)技术背景下。本文深入探讨了人工智能对医疗实践的影响,并通过分析提出了中国职业医学教育体系的改革建议。研究发现,人工智能能显著提高诊断能力、治疗决策和患者管理水平。然而,人工智能也带来了一些问题,如潜在的工作岗位流失,以及医疗专业人员必须适应新技术。建议的改革包括更加注重批判性思维、实践经验、技能培养、医学伦理,以及将人文学科和人工智能纳入课程。这些改革需要持续的评估和持续的研究,以有效地培养医学生应对未来的挑战。
{"title":"Reforming China's Secondary Vocational Medical Education: Adapting to the Challenges and Opportunities of the AI Era.","authors":"Wenting Tong, Xiaowen Zhang, Haiping Zeng, Jianping Pan, Chao Gong, Hui Zhang","doi":"10.2196/48594","DOIUrl":"10.2196/48594","url":null,"abstract":"<p><strong>Unlabelled: </strong>China's secondary vocational medical education is essential for training primary health care personnel and enhancing public health responses. This education system currently faces challenges, primarily due to its emphasis on knowledge acquisition that overshadows the development and application of skills, especially in the context of emerging artificial intelligence (AI) technologies. This article delves into the impact of AI on medical practices and uses this analysis to suggest reforms for the vocational medical education system in China. AI is found to significantly enhance diagnostic capabilities, therapeutic decision-making, and patient management. However, it also brings about concerns such as potential job losses and necessitates the adaptation of medical professionals to new technologies. Proposed reforms include a greater focus on critical thinking, hands-on experiences, skill development, medical ethics, and integrating humanities and AI into the curriculum. These reforms require ongoing evaluation and sustained research to effectively prepare medical students for future challenges in the field.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11337726/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141989105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Impact of a New Gynecologic Oncology Hashtag During Virtual-Only ASCO Annual Meetings: An X (Twitter) Social Network Analysis. 新的妇科肿瘤学标签在虚拟ASCO年会期间的影响:X(Twitter)社交网络分析。
IF 3.2 Q1 EDUCATION, SCIENTIFIC DISCIPLINES Pub Date : 2024-08-14 DOI: 10.2196/45291
Geetu Bhandoria, Esra Bilir, Christina Uwins, Josep Vidal-Alaball, Aïna Fuster-Casanovas, Wasim Ahmed

Background: Official conference hashtags are commonly used to promote tweeting and social media engagement. The reach and impact of introducing a new hashtag during an oncology conference have yet to be studied. The American Society of Clinical Oncology (ASCO) conducts an annual global meeting, which was entirely virtual due to the COVID-19 pandemic in 2020 and 2021.

Objective: This study aimed to assess the reach and impact (in the form of vertices and edges generated) and X (formerly Twitter) activity of the new hashtags #goASCO20 and #goASCO21 in the ASCO 2020 and 2021 virtual conferences.

Methods: New hashtags (#goASCO20 and #goASCO21) were created for the ASCO virtual conferences in 2020 and 2021 to help focus gynecologic oncology discussion at the ASCO meetings. Data were retrieved using these hashtags (#goASCO20 for 2020 and #goASCO21 for 2021). A social network analysis was performed using the NodeXL software application.

Results: The hashtags #goASCO20 and #goASCO21 had similar impacts on the social network. Analysis of the reach and impact of the individual hashtags found #goASCO20 to have 150 vertices and 2519 total edges and #goASCO20 to have 174 vertices and 2062 total edges. Mentions and tweets between 2020 and 2021 were also similar. The circles representing different users were spatially arranged in a more balanced way in 2021. Tweets using the #goASCO21 hashtag received significantly more responses than tweets using #goASCO20 (75 times in 2020 vs 360 times in 2021; z value=16.63 and P<.001). This indicates increased engagement in the subsequent year.

Conclusions: Introducing a gynecologic oncology specialty-specific hashtag (#goASCO20 and #goASCO21) that is related but different from the official conference hashtag (#ASCO20 and #ASCO21) helped facilitate discussion on topics of interest to gynecologic oncologists during a virtual pan-oncology meeting. This impact was visible in the social network analysis.

背景:官方会议标签通常用于促进推特和社交媒体的参与。在肿瘤学会议期间引入新标签的覆盖范围和影响尚待研究。美国临床肿瘤学会(ASCO)每年举行一次全球会议,由于 COVID-19 大流行,2020 年和 2021 年的会议完全是虚拟的:本研究旨在评估 ASCO 2020 年和 2021 年虚拟会议中新标签 #goASCO20 和 #goASCO21 的覆盖范围和影响(以产生的顶点和边的形式)以及 X(原 Twitter)活动:为 2020 年和 2021 年 ASCO 虚拟会议创建了新标签(#goASCO20 和 #goASCO21),以帮助 ASCO 会议集中讨论妇科肿瘤学。使用这些标签(2020 年为 #goASCO20,2021 年为 #goASCO21)检索了数据。使用 NodeXL 软件应用程序进行了社交网络分析:结果:#goASCO20 和 #goASCO21 标签对社交网络的影响相似。对单个标签的覆盖范围和影响进行分析后发现,#goASCO20 有 150 个顶点和 2519 条边,#goASCO20 有 174 个顶点和 2062 条边。2020 年和 2021 年之间的提及和推文也很相似。2021 年,代表不同用户的圆圈在空间排列上更加均衡。使用 #goASCO21 标签的推文收到的回复明显多于使用 #goASCO20 的推文(2020 年为 75 次,2021 年为 360 次;z 值=16.63,PConclusions:在虚拟泛肿瘤学会议期间,引入与官方会议标签(#ASCO20 和 #ASCO21)相关但又不同的妇科肿瘤专科标签(#goASCO20 和 #goASCO21)有助于促进妇科肿瘤学家就感兴趣的话题展开讨论。这种影响在社交网络分析中显而易见。
{"title":"Impact of a New Gynecologic Oncology Hashtag During Virtual-Only ASCO Annual Meetings: An X (Twitter) Social Network Analysis.","authors":"Geetu Bhandoria, Esra Bilir, Christina Uwins, Josep Vidal-Alaball, Aïna Fuster-Casanovas, Wasim Ahmed","doi":"10.2196/45291","DOIUrl":"10.2196/45291","url":null,"abstract":"<p><strong>Background: </strong>Official conference hashtags are commonly used to promote tweeting and social media engagement. The reach and impact of introducing a new hashtag during an oncology conference have yet to be studied. The American Society of Clinical Oncology (ASCO) conducts an annual global meeting, which was entirely virtual due to the COVID-19 pandemic in 2020 and 2021.</p><p><strong>Objective: </strong>This study aimed to assess the reach and impact (in the form of vertices and edges generated) and X (formerly Twitter) activity of the new hashtags #goASCO20 and #goASCO21 in the ASCO 2020 and 2021 virtual conferences.</p><p><strong>Methods: </strong>New hashtags (#goASCO20 and #goASCO21) were created for the ASCO virtual conferences in 2020 and 2021 to help focus gynecologic oncology discussion at the ASCO meetings. Data were retrieved using these hashtags (#goASCO20 for 2020 and #goASCO21 for 2021). A social network analysis was performed using the NodeXL software application.</p><p><strong>Results: </strong>The hashtags #goASCO20 and #goASCO21 had similar impacts on the social network. Analysis of the reach and impact of the individual hashtags found #goASCO20 to have 150 vertices and 2519 total edges and #goASCO20 to have 174 vertices and 2062 total edges. Mentions and tweets between 2020 and 2021 were also similar. The circles representing different users were spatially arranged in a more balanced way in 2021. Tweets using the #goASCO21 hashtag received significantly more responses than tweets using #goASCO20 (75 times in 2020 vs 360 times in 2021; z value=16.63 and P<.001). This indicates increased engagement in the subsequent year.</p><p><strong>Conclusions: </strong>Introducing a gynecologic oncology specialty-specific hashtag (#goASCO20 and #goASCO21) that is related but different from the official conference hashtag (#ASCO20 and #ASCO21) helped facilitate discussion on topics of interest to gynecologic oncologists during a virtual pan-oncology meeting. This impact was visible in the social network analysis.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11339558/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141989104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Influence of Model Evolution and System Roles on ChatGPT's Performance in Chinese Medical Licensing Exams: Comparative Study. 模式演变和系统角色对中国医师资格考试中 ChatGPT 成绩的影响:比较研究。
IF 3.2 Q1 EDUCATION, SCIENTIFIC DISCIPLINES Pub Date : 2024-08-13 DOI: 10.2196/52784
Shuai Ming, Qingge Guo, Wenjun Cheng, Bo Lei

Background: With the increasing application of large language models like ChatGPT in various industries, its potential in the medical domain, especially in standardized examinations, has become a focal point of research.

Objective: The aim of this study is to assess the clinical performance of ChatGPT, focusing on its accuracy and reliability in the Chinese National Medical Licensing Examination (CNMLE).

Methods: The CNMLE 2022 question set, consisting of 500 single-answer multiple choices questions, were reclassified into 15 medical subspecialties. Each question was tested 8 to 12 times in Chinese on the OpenAI platform from April 24 to May 15, 2023. Three key factors were considered: the version of GPT-3.5 and 4.0, the prompt's designation of system roles tailored to medical subspecialties, and repetition for coherence. A passing accuracy threshold was established as 60%. The χ2 tests and κ values were employed to evaluate the model's accuracy and consistency.

Results: GPT-4.0 achieved a passing accuracy of 72.7%, which was significantly higher than that of GPT-3.5 (54%; P<.001). The variability rate of repeated responses from GPT-4.0 was lower than that of GPT-3.5 (9% vs 19.5%; P<.001). However, both models showed relatively good response coherence, with κ values of 0.778 and 0.610, respectively. System roles numerically increased accuracy for both GPT-4.0 (0.3%-3.7%) and GPT-3.5 (1.3%-4.5%), and reduced variability by 1.7% and 1.8%, respectively (P>.05). In subgroup analysis, ChatGPT achieved comparable accuracy among different question types (P>.05). GPT-4.0 surpassed the accuracy threshold in 14 of 15 subspecialties, while GPT-3.5 did so in 7 of 15 on the first response.

Conclusions: GPT-4.0 passed the CNMLE and outperformed GPT-3.5 in key areas such as accuracy, consistency, and medical subspecialty expertise. Adding a system role insignificantly enhanced the model's reliability and answer coherence. GPT-4.0 showed promising potential in medical education and clinical practice, meriting further study.

研究背景随着大型语言模型(如 ChatGPT)在各行各业的应用日益广泛,其在医学领域,尤其是标准化考试中的潜力已成为研究的焦点:本研究旨在评估 ChatGPT 的临床表现,重点关注其在中国国家医师资格考试(CNMLE)中的准确性和可靠性:中国国家医师资格考试(CNMLE)2022年试题集由500道单项选择题组成,并重新分为15个医学亚专业。2023 年 4 月 24 日至 5 月 15 日,在 OpenAI 平台上对每道题进行了 8-12 次中文测试。测试中考虑了三个关键因素:GPT-3.5 和 4.0 版本、根据医学亚专科指定系统角色的提示以及为保持连贯性而进行的重复。通过准确率阈值定为 60%。采用χ2检验和κ值来评估模型的准确性和一致性:GPT-4.0的通过准确率为72.7%,明显高于GPT-3.5(54%;P.05)。在分组分析中,不同题型的 ChatGPT 准确率相当(P>.05)。GPT-4.0 在 15 个亚专科中的 14 个超过了准确率阈值,而 GPT-3.5 则在 15 个亚专科中的 7 个首次回答就超过了准确率阈值:结论:GPT-4.0 通过了 CNMLE 考试,并在准确性、一致性和医学亚专科专业知识等关键领域优于 GPT-3.5。添加系统角色对模型的可靠性和答案一致性的提升并不明显。GPT-4.0 在医学教育和临床实践中表现出了巨大的潜力,值得进一步研究。
{"title":"Influence of Model Evolution and System Roles on ChatGPT's Performance in Chinese Medical Licensing Exams: Comparative Study.","authors":"Shuai Ming, Qingge Guo, Wenjun Cheng, Bo Lei","doi":"10.2196/52784","DOIUrl":"10.2196/52784","url":null,"abstract":"<p><strong>Background: </strong>With the increasing application of large language models like ChatGPT in various industries, its potential in the medical domain, especially in standardized examinations, has become a focal point of research.</p><p><strong>Objective: </strong>The aim of this study is to assess the clinical performance of ChatGPT, focusing on its accuracy and reliability in the Chinese National Medical Licensing Examination (CNMLE).</p><p><strong>Methods: </strong>The CNMLE 2022 question set, consisting of 500 single-answer multiple choices questions, were reclassified into 15 medical subspecialties. Each question was tested 8 to 12 times in Chinese on the OpenAI platform from April 24 to May 15, 2023. Three key factors were considered: the version of GPT-3.5 and 4.0, the prompt's designation of system roles tailored to medical subspecialties, and repetition for coherence. A passing accuracy threshold was established as 60%. The χ2 tests and κ values were employed to evaluate the model's accuracy and consistency.</p><p><strong>Results: </strong>GPT-4.0 achieved a passing accuracy of 72.7%, which was significantly higher than that of GPT-3.5 (54%; P<.001). The variability rate of repeated responses from GPT-4.0 was lower than that of GPT-3.5 (9% vs 19.5%; P<.001). However, both models showed relatively good response coherence, with κ values of 0.778 and 0.610, respectively. System roles numerically increased accuracy for both GPT-4.0 (0.3%-3.7%) and GPT-3.5 (1.3%-4.5%), and reduced variability by 1.7% and 1.8%, respectively (P>.05). In subgroup analysis, ChatGPT achieved comparable accuracy among different question types (P>.05). GPT-4.0 surpassed the accuracy threshold in 14 of 15 subspecialties, while GPT-3.5 did so in 7 of 15 on the first response.</p><p><strong>Conclusions: </strong>GPT-4.0 passed the CNMLE and outperformed GPT-3.5 in key areas such as accuracy, consistency, and medical subspecialty expertise. Adding a system role insignificantly enhanced the model's reliability and answer coherence. GPT-4.0 showed promising potential in medical education and clinical practice, meriting further study.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11336778/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141976840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Understanding Health Care Students' Perceptions, Beliefs, and Attitudes Toward AI-Powered Language Models: Cross-Sectional Study. 了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。
IF 3.2 Q1 EDUCATION, SCIENTIFIC DISCIPLINES Pub Date : 2024-08-13 DOI: 10.2196/51757
Ivan Cherrez-Ojeda, Juan C Gallardo-Bastidas, Karla Robles-Velasco, María F Osorio, Eleonor Maria Velez Leon, Manuel Leon Velastegui, Patrícia Pauletto, F C Aguilar-Díaz, Aldo Squassi, Susana Patricia González Eras, Erita Cordero Carrasco, Karol Leonor Chavez Gonzalez, Juan C Calderon, Jean Bousquet, Anna Bedbrook, Marco Faytong-Haro
<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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).</p><p><strong>Conclusions: </strong>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 mus
背景:ChatGPT 并不打算用于医疗保健领域,但它的潜在优势取决于最终用户的理解和接受程度,而这正是医疗保健专业学生的关键所在。这方面的研究还很有限:我们研究的主要目的是评估在卫生领域教育背景下使用 ChatGPT 的频率、认知水平、与使用 ChatGPT 相关的风险、伦理问题以及对使用 ChatGPT 的态度。此外,我们还旨在研究不同群体之间是否存在基于人口统计学变量的差异。研究的第二部分旨在评估使用频率、感知知识水平、风险感知水平和道德感知水平与参与者对使用 ChatGPT 的态度之间的关联:于 2023 年 5 月至 6 月进行了一项横断面调查,调查对象包括美洲各地的医学、护理学、牙医学、营养学和实验室科学专业的学生。研究采用了描述性分析、卡方检验和方差分析来评估不同类别之间的统计意义。研究使用了多个序数逻辑回归模型来分析预测因素(使用频率、知识感知、风险感知和道德感知得分)对因变量态度的影响。模型根据性别、机构类型、专业和国家进行了调整。所有分析均使用 Stata 进行:在 2661 名医护学生中,42.99%(n=1144)的学生不了解 ChatGPT。了解程度的中位数为 "最低"(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,也不违背伦理。大多数参与者(中位数 3.89,IQR 3.44-4.34)"有点同意 "ChatGPT(1)有益于医疗机构,(2)提供值得信赖的数据,(3)是临床和教育医疗信息获取的有用工具,(4)使工作更轻松。共有 70% (7/10)的人将其用于家庭作业。随着对 ChatGPT 认知的增加,人们对 ChatGPT 的好感度也在增加。较高的道德考量感知评分增加了将 ChatGPT 视为值得信赖的医疗保健信息来源(几率比 [OR] 1.620,95% CI 1.498-1.752)、有益于医疗问题(OR 1.495,95% CI 1.452-1.539)和对医学文献有用(OR 1.494,95% CI 1.426-1.564;PC 结论:尽管 ChatGPT 在医疗领域得到了广泛应用,但仍有超过 40% 的美国医学生(1144/2661,42.99%)不了解 ChatGPT。我们的数据显示了学生们对 ChatGPT 的积极态度和进一步了解它的愿望。医学教育工作者必须探索如何将聊天机器人纳入本科医疗保健教育课程。
{"title":"Understanding Health Care Students' Perceptions, Beliefs, and Attitudes Toward AI-Powered Language Models: Cross-Sectional Study.","authors":"Ivan Cherrez-Ojeda, Juan C Gallardo-Bastidas, Karla Robles-Velasco, María F Osorio, Eleonor Maria Velez Leon, Manuel Leon Velastegui, Patrícia Pauletto, F C Aguilar-Díaz, Aldo Squassi, Susana Patricia González Eras, Erita Cordero Carrasco, Karol Leonor Chavez Gonzalez, Juan C Calderon, Jean Bousquet, Anna Bedbrook, Marco Faytong-Haro","doi":"10.2196/51757","DOIUrl":"10.2196/51757","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;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&lt;.001 for all results).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;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 mus","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11350293/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141971968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Educational Utility of Clinical Vignettes Generated in Japanese by ChatGPT-4: Mixed Methods Study. 由 ChatGPT-4 生成的日语临床小故事的教育效用:混合方法研究。
IF 3.2 Q1 EDUCATION, SCIENTIFIC DISCIPLINES Pub Date : 2024-08-13 DOI: 10.2196/59133
Hiromizu Takahashi, Kiyoshi Shikino, Takeshi Kondo, Akira Komori, Yuji Yamada, Mizue Saita, Toshio Naito

Background: Evaluating the accuracy and educational utility of artificial intelligence-generated medical cases, especially those produced by large language models such as ChatGPT-4 (developed by OpenAI), is crucial yet underexplored.

Objective: This study aimed to assess the educational utility of ChatGPT-4-generated clinical vignettes and their applicability in educational settings.

Methods: Using a convergent mixed methods design, a web-based survey was conducted from January 8 to 28, 2024, to evaluate 18 medical cases generated by ChatGPT-4 in Japanese. In the survey, 6 main question items were used to evaluate the quality of the generated clinical vignettes and their educational utility, which are information quality, information accuracy, educational usefulness, clinical match, terminology accuracy (TA), and diagnosis difficulty. Feedback was solicited from physicians specializing in general internal medicine or general medicine and experienced in medical education. Chi-square and Mann-Whitney U tests were performed to identify differences among cases, and linear regression was used to examine trends associated with physicians' experience. Thematic analysis of qualitative feedback was performed to identify areas for improvement and confirm the educational utility of the cases.

Results: Of the 73 invited participants, 71 (97%) responded. The respondents, primarily male (64/71, 90%), spanned a broad range of practice years (from 1976 to 2017) and represented diverse hospital sizes throughout Japan. The majority deemed the information quality (mean 0.77, 95% CI 0.75-0.79) and information accuracy (mean 0.68, 95% CI 0.65-0.71) to be satisfactory, with these responses being based on binary data. The average scores assigned were 3.55 (95% CI 3.49-3.60) for educational usefulness, 3.70 (95% CI 3.65-3.75) for clinical match, 3.49 (95% CI 3.44-3.55) for TA, and 2.34 (95% CI 2.28-2.40) for diagnosis difficulty, based on a 5-point Likert scale. Statistical analysis showed significant variability in content quality and relevance across the cases (P<.001 after Bonferroni correction). Participants suggested improvements in generating physical findings, using natural language, and enhancing medical TA. The thematic analysis highlighted the need for clearer documentation, clinical information consistency, content relevance, and patient-centered case presentations.

Conclusions: ChatGPT-4-generated medical cases written in Japanese possess considerable potential as resources in medical education, with recognized adequacy in quality and accuracy. Nevertheless, there is a notable need for enhancements in the precision and realism of case details. This study emphasizes ChatGPT-4's value as an adjunctive educational tool in the medical field, requiring expert oversight for optimal application.

背景:评估人工智能生成的医学病例,尤其是由大型语言模型(如ChatGPT-4,由OpenAI开发)生成的医学病例的准确性和教育效用至关重要,但这方面的探索还不够:本研究旨在评估 ChatGPT-4 生成的临床案例的教育效用及其在教育环境中的适用性:方法:采用聚合混合方法设计,于 2024 年 1 月 8 日至 28 日开展了一项基于网络的调查,对 ChatGPT-4 生成的 18 个日语医疗案例进行了评估。调查中使用了 6 个主要问题项目来评估生成的临床案例的质量及其教育实用性,即信息质量、信息准确性、教育实用性、临床匹配性、术语准确性(TA)和诊断难度。我们向具有医学教育经验的普通内科或全科医生征求了反馈意见。采用卡方检验(Chi-square)和曼-惠特尼U检验(Mann-Whitney U)来确定不同病例之间的差异,并使用线性回归来研究与医生经验相关的趋势。对定性反馈进行了主题分析,以确定需要改进的地方,并确认案例的教育效用:在 73 名受邀参与者中,有 71 人(97%)做出了回应。受访者主要为男性(64/71,90%),执业年限跨度很大(从 1976 年到 2017 年),代表了日本各地不同规模的医院。大多数人认为信息质量(平均值 0.77,95% CI 0.75-0.79)和信息准确性(平均值 0.68,95% CI 0.65-0.71)令人满意,这些回答基于二进制数据。根据 5 点李克特量表,教育有用性的平均得分为 3.55(95% CI 3.49-3.60),临床匹配度为 3.70(95% CI 3.65-3.75),TA 为 3.49(95% CI 3.44-3.55),诊断难度为 2.34(95% CI 2.28-2.40)。统计分析表明,不同病例在内容质量和相关性方面存在明显差异(PC 结论:用日语编写的 ChatGPT-4 生成的医学病例在质量和准确性方面都得到了认可,具有作为医学教育资源的巨大潜力。然而,在病例细节的精确性和真实性方面仍有明显的改进需求。本研究强调了 ChatGPT-4 作为医学领域辅助教育工具的价值,需要专家的监督才能达到最佳应用效果。
{"title":"Educational Utility of Clinical Vignettes Generated in Japanese by ChatGPT-4: Mixed Methods Study.","authors":"Hiromizu Takahashi, Kiyoshi Shikino, Takeshi Kondo, Akira Komori, Yuji Yamada, Mizue Saita, Toshio Naito","doi":"10.2196/59133","DOIUrl":"10.2196/59133","url":null,"abstract":"<p><strong>Background: </strong>Evaluating the accuracy and educational utility of artificial intelligence-generated medical cases, especially those produced by large language models such as ChatGPT-4 (developed by OpenAI), is crucial yet underexplored.</p><p><strong>Objective: </strong>This study aimed to assess the educational utility of ChatGPT-4-generated clinical vignettes and their applicability in educational settings.</p><p><strong>Methods: </strong>Using a convergent mixed methods design, a web-based survey was conducted from January 8 to 28, 2024, to evaluate 18 medical cases generated by ChatGPT-4 in Japanese. In the survey, 6 main question items were used to evaluate the quality of the generated clinical vignettes and their educational utility, which are information quality, information accuracy, educational usefulness, clinical match, terminology accuracy (TA), and diagnosis difficulty. Feedback was solicited from physicians specializing in general internal medicine or general medicine and experienced in medical education. Chi-square and Mann-Whitney U tests were performed to identify differences among cases, and linear regression was used to examine trends associated with physicians' experience. Thematic analysis of qualitative feedback was performed to identify areas for improvement and confirm the educational utility of the cases.</p><p><strong>Results: </strong>Of the 73 invited participants, 71 (97%) responded. The respondents, primarily male (64/71, 90%), spanned a broad range of practice years (from 1976 to 2017) and represented diverse hospital sizes throughout Japan. The majority deemed the information quality (mean 0.77, 95% CI 0.75-0.79) and information accuracy (mean 0.68, 95% CI 0.65-0.71) to be satisfactory, with these responses being based on binary data. The average scores assigned were 3.55 (95% CI 3.49-3.60) for educational usefulness, 3.70 (95% CI 3.65-3.75) for clinical match, 3.49 (95% CI 3.44-3.55) for TA, and 2.34 (95% CI 2.28-2.40) for diagnosis difficulty, based on a 5-point Likert scale. Statistical analysis showed significant variability in content quality and relevance across the cases (P<.001 after Bonferroni correction). Participants suggested improvements in generating physical findings, using natural language, and enhancing medical TA. The thematic analysis highlighted the need for clearer documentation, clinical information consistency, content relevance, and patient-centered case presentations.</p><p><strong>Conclusions: </strong>ChatGPT-4-generated medical cases written in Japanese possess considerable potential as resources in medical education, with recognized adequacy in quality and accuracy. Nevertheless, there is a notable need for enhancements in the precision and realism of case details. This study emphasizes ChatGPT-4's value as an adjunctive educational tool in the medical field, requiring expert oversight for optimal application.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11350316/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141971965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Resources to Support Canadian Nurses to Deliver Virtual Care: Environmental Scan. 支持加拿大护士提供虚拟护理的资源:环境扫描。
IF 3.2 Q1 EDUCATION, SCIENTIFIC DISCIPLINES Pub Date : 2024-08-13 DOI: 10.2196/53254
Manal Kleib, Antonia Arnaert, Lynn M Nagle, Elizabeth Mirekuwaa Darko, Sobia Idrees, Daniel da Costa, Shamsa Ali
<p><strong>Background: </strong>Regulatory and professional nursing associations have an important role in ensuring that nurses provide safe, competent, and ethical care and are capable of adapting to emerging phenomena that influence society and population health needs. Telehealth and more recently virtual care are 2 digital health modalities that have gained momentum during the COVID-19 pandemic. Telehealth refers to telecommunications and digital communication technologies used to deliver health care, support health care provider and patient education, and facilitate self-care. Virtual care facilitates the delivery of health care services via any remote communication between patients and health care providers and among health care providers, either synchronously or asynchronously, through information and communication technologies. Despite nurses' adaptability to delivering virtual care, many have also reported challenges.</p><p><strong>Objective: </strong>This study aims to describe resources about virtual care, digital health, and nursing informatics (ie, practice guidelines and fact sheets) available to Canadian nurses through their regulatory and professional associations.</p><p><strong>Methods: </strong>An environmental scan was conducted between March and July 2023. The websites of nursing regulatory bodies across 13 Canadian provinces and territories and relevant nursing and a few nonnursing professional associations were searched. Data were extracted from the websites of these organizations to map out educational materials, training opportunities, and guidelines made available for nurses to learn and adapt to the ongoing digitalization of the health care system. Information from each source was summarized and analyzed using an inductive content analysis approach to identify categories and themes. The Virtual Health Competency Framework was applied to support the analysis process.</p><p><strong>Results: </strong>Seven themes were identified: (1) types of resources available about virtual care, (2) terminologies used in virtual care resources, (3) currency of virtual care resources identified, (4) requirements for providing virtual care between provinces, (5) resources through professional nursing associations and other relevant organizations, (6) regulatory guidance versus competency in virtual care, and (7) resources about digital health and nursing informatics. Results also revealed that practice guidance for delivering telehealth existed before the COVID-19 pandemic, but it was further expanded during the pandemic. Differences were noted across available resources with respect to terms used (eg, telenursing, telehealth, or virtual care), types of documents (eg, guideline vs fact sheet), and the depth of information shared. Only 2 associations provided comprehensive telenursing practice guidelines. Resources relative to digital health and nursing informatics exist, but variations between provinces were also noted.</p><p><strong>Conclu
背景:监管机构和专业护理协会在确保护士提供安全、称职和合乎道德的护理方面发挥着重要作用,并有能力适应影响社会和人口健康需求的新现象。远程医疗和最近的虚拟护理是在 COVID-19 大流行期间势头强劲的两种数字医疗模式。远程保健是指用于提供保健服务、支持保健服务提供者和患者教育以及促进自我保健的电信和数字通信技术。虚拟医疗通过信息和通信技术,在患者和医疗服务提供者之间以及医疗服务提供者之间进行同步或非同步的远程通信,为提供医疗服务提供便利。尽管护士对提供虚拟医疗服务的适应性很强,但许多护士也表示面临挑战:本研究旨在描述加拿大护士可通过其监管和专业协会获得的有关虚拟护理、数字健康和护理信息学的资源(即实践指南和概况介绍):方法:在 2023 年 3 月至 7 月期间进行了一次环境扫描。方法:在 2023 年 3 月至 7 月期间进行了一次环境扫描,搜索了加拿大 13 个省和地区的护理监管机构以及相关的护理专业协会和一些非护理专业协会的网站。从这些机构的网站上提取数据,以绘制出教育材料、培训机会和指南,供护士学习和适应正在进行的医疗保健系统数字化。采用归纳式内容分析法对每个来源的信息进行总结和分析,以确定类别和主题。虚拟医疗能力框架被用于支持分析过程:结果:确定了七个主题:(1) 有关虚拟护理的可用资源类型,(2) 虚拟护理资源中使用的术语,(3) 确定的虚拟护理资源的时效性,(4) 不同省份之间提供虚拟护理的要求,(5) 通过专业护理协会和其他相关组织提供的资源,(6) 有关虚拟护理能力的监管指导,以及 (7) 有关数字健康和护理信息学的资源。结果还显示,在 COVID-19 大流行之前,就有提供远程医疗的实践指导,但在大流行期间进一步扩大。在使用的术语(如远程护理、远程医疗或虚拟护理)、文件类型(如指南与概况介绍)以及共享信息的深度方面,现有资源存在差异。只有两个协会提供了全面的远程护理实践指南。虽然存在与数字健康和护理信息学相关的资源,但各省之间也存在差异:结论:远程医疗和虚拟护理服务的使用正在成为加拿大医疗保健的主流。尽管各辖区之间存在差异,但现有的用于提供远程医疗和虚拟护理的护理实践指导资源非常丰富,可以作为开发一套标准化实践要求或能力的开端,为护理实践和未来护士的教育提供参考。
{"title":"Resources to Support Canadian Nurses to Deliver Virtual Care: Environmental Scan.","authors":"Manal Kleib, Antonia Arnaert, Lynn M Nagle, Elizabeth Mirekuwaa Darko, Sobia Idrees, Daniel da Costa, Shamsa Ali","doi":"10.2196/53254","DOIUrl":"10.2196/53254","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Regulatory and professional nursing associations have an important role in ensuring that nurses provide safe, competent, and ethical care and are capable of adapting to emerging phenomena that influence society and population health needs. Telehealth and more recently virtual care are 2 digital health modalities that have gained momentum during the COVID-19 pandemic. Telehealth refers to telecommunications and digital communication technologies used to deliver health care, support health care provider and patient education, and facilitate self-care. Virtual care facilitates the delivery of health care services via any remote communication between patients and health care providers and among health care providers, either synchronously or asynchronously, through information and communication technologies. Despite nurses' adaptability to delivering virtual care, many have also reported challenges.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to describe resources about virtual care, digital health, and nursing informatics (ie, practice guidelines and fact sheets) available to Canadian nurses through their regulatory and professional associations.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;An environmental scan was conducted between March and July 2023. The websites of nursing regulatory bodies across 13 Canadian provinces and territories and relevant nursing and a few nonnursing professional associations were searched. Data were extracted from the websites of these organizations to map out educational materials, training opportunities, and guidelines made available for nurses to learn and adapt to the ongoing digitalization of the health care system. Information from each source was summarized and analyzed using an inductive content analysis approach to identify categories and themes. The Virtual Health Competency Framework was applied to support the analysis process.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Seven themes were identified: (1) types of resources available about virtual care, (2) terminologies used in virtual care resources, (3) currency of virtual care resources identified, (4) requirements for providing virtual care between provinces, (5) resources through professional nursing associations and other relevant organizations, (6) regulatory guidance versus competency in virtual care, and (7) resources about digital health and nursing informatics. Results also revealed that practice guidance for delivering telehealth existed before the COVID-19 pandemic, but it was further expanded during the pandemic. Differences were noted across available resources with respect to terms used (eg, telenursing, telehealth, or virtual care), types of documents (eg, guideline vs fact sheet), and the depth of information shared. Only 2 associations provided comprehensive telenursing practice guidelines. Resources relative to digital health and nursing informatics exist, but variations between provinces were also noted.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclu","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11350304/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141971967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
期刊
JMIR Medical Education
全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1