JMIR Medical Education最新文献_第4页

Combining Problem-Based Learning Methods With the WeChat Platform in Teaching Ophthalmology: Randomized Controlled Trial. 基于问题的学习方法与微信平台在眼科教学中的结合：随机对照试验。

IF 3.2 Q1 EDUCATION, SCIENTIFIC DISCIPLINES

JMIR Medical Education

Pub Date : 2026-01-05 DOI: 10.2196/65279

Fang Fang, Bing Bu, Wenmin Jiang

Background: Ophthalmology poses distinct learning challenges for medical students due to the complex anatomy of the eye and the requirement of essential hands-on skills. Problem-based learning (PBL), a student-centered approach, fosters clinical reasoning and self-directed learning. To address the time and logistical constraints of traditional teaching methods, this study implemented a WeChat-based PBL model that leveraged the platform's efficiency and interactivity to enhance student engagement and skill acquisition in ophthalmology.Objective: This study aimed to evaluate the effectiveness of WeChat-based PBL in ophthalmology education, focusing on its impact on students' self-perception of learning and clinical skills compared to traditional teaching methods.Methods: This study involved 108 undergraduate students who successfully passed the Chinese National Entrance Examination. Among them, 54 (50%) were randomly selected to participate in the WeChat-based PBL, while the other 54 (50%) received traditional teaching. Students were placed into 6 groups (18 students for each group) using a random number table, and the new teaching methods were tested outside their regular class time. Three groups were randomly selected to receive PBL using WeChat as the platform, while the remaining 3 groups received conventional teaching.Results: Our analysis indicated that although students in the WeChat-based PBL group scored marginally lower in memorization compared to their peers in the traditional teaching group (traditional group: mean 37.6, SD 2.8; WeChat group: mean 32.0, SD 4.1; P=.006; n=54), they exhibited markedly superior levels of understanding (traditional group: mean 24.1, SD 1.8; WeChat group: mean 28.0, SD 1.3; P=.007; n=54) and knowledge application (traditional group: mean 24.3, SD 1.9; WeChat group: mean 27.6, SD 1.3; P=.008; n=54). This suggests that the WeChat-based PBL method promotes deeper engagement, enabling students to better comprehend essential concepts, even with a diminished emphasis on rote learning. Additionally, students in the WeChat group reported increased collaboration (traditional group: mean 3.8889, SD 0.8393; WeChat group: mean 1.7222, SD 0.5961; P<.001); motivation (traditional group: mean 3.5471, SD 0.7915; WeChat group: mean 1.8333, SD 0.5746; P=.004); knowledge acquisition (traditional group: mean 3.6667, SD 0.7770; WeChat group: mean 1.8704, SD 0.7017; P<.001); self-learning ability (traditional group: mean 3.5741, SD 0.7673; WeChat group: mean 1.8519, SD 0.4917; P<.001); clinical reasoning (traditional group: mean 2.9444, SD 0.8777; WeChat group: mean 1.9630, SD 0.6132; P=.01); and problem-solving skills (traditional group: mean 3.2037, SD 0.6553; WeChat group: mean 1.8519, SD 0.5287; P=.001) than the students in the traditional group.Conclusions: Integrating PBL methods with WeChat has been shown to

背景：由于眼睛的复杂解剖结构和对基本实践技能的要求，眼科对医学生提出了独特的学习挑战。基于问题的学习（PBL）是一种以学生为中心的方法，培养临床推理和自主学习。为了解决传统教学方法的时间和后勤限制，本研究实施了一种基于微信的PBL模型，利用平台的效率和互动性来提高学生在眼科的参与度和技能习得。目的：本研究旨在评估基于微信的PBL在眼科教育中的有效性，重点研究与传统教学方法相比，微信PBL对学生自我学习感知和临床技能的影响。方法：以108名高考在校生为研究对象。其中随机抽取54人（50%）参与微信PBL，另外54人（50%）接受传统教学。使用随机数字表将学生分成6组（每组18名学生），并在常规上课时间之外对新教学方法进行测试。随机选择3组以微信为平台进行PBL教学，其余3组进行常规教学。结果：我们的分析表明，虽然微信PBL组学生在记忆方面的得分略低于传统教学组（传统组：平均37.6,SD 2.8；微信组：平均32.0,SD 4.1； P= 0.006; n=54），但他们的理解水平（传统组：平均24.1,SD 1.8；微信组：平均28.0,SD 1.3； P= 0.007; n=54）和知识应用水平(传统组：平均24.3,SD 1.9；微信组：平均值27.6，标准差1.3；P = .008;n = 54)。这表明基于微信的PBL方法促进了更深层次的参与，使学生更好地理解基本概念，即使减少了对死记硬背学习的强调。微信组：平均1.7222,SD 0.5961。结论：与传统教学相比，将PBL方法与微信相结合，可以提高眼科教育的效果，表明该方法可能是传统教学的一种更好的替代方法。

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引用次数: 0

Live Podcasting as an Educational Intervention in Dentomaxillofacial Radiology: Controlled Cohort Study. 直播播客作为牙颌面放射学的教育干预：对照队列研究。

IF 3.2 Q1 EDUCATION, SCIENTIFIC DISCIPLINES

JMIR Medical Education

Pub Date : 2026-01-05 DOI: 10.2196/77980

Anna-Lena Hillebrecht, Daniel Fritzsche, Thamar Voss, Anne Kruse, Andreas Keßler, Kirstin Vach, Markus Jörg Altenburger, Rainer Schmelzeisen, Wiebke Semper-Hogg

Background: Podcasts are increasingly used in health professions education; however, most formats are asynchronous and noninteractive. Didactically grounded, synchronous implementations in dental curricula are scarce.

Objective: This study aims to design, implement, and evaluate a synchronous, case-based live podcast (LP) as a didactic teaching format in dentomaxillofacial radiology.

Methods: In a controlled cohort study with 2 third-year cohorts (N=41), the intervention group (IG; n=21, 51%) received weekly case-based LP sessions in addition to standard teaching, while the control group (CG; n=20, 49%) received standard teaching only. Acceptability was evaluated 6 months postcourse using the 27-item student evaluation questionnaire and open-text responses. Knowledge was assessed immediately after the course with a 21-item radiology knowledge test, and after 6 months, with a 15-item interdisciplinary clinical application test.

Results: The primary outcome was student-reported acceptability of the LP format. It was rated highly by students in the Student Evaluation Questionnaire (mean out of 10: structure 9.76, interactivity 9.62, interdisciplinary relevance 9.55). Qualitative feedback was assessed highlighting motivation, authenticity, and discussion quality. In the radiology knowledge test, no group differences were observed (IG: n=21, 51% vs CG: n=20, 49%; P=.37). In the interdisciplinary clinical application test, the IG outperformed the CG in restorative dentistry (median 5, IQR 4-5 vs median 4, IQR 3-5; P=.02; r=0.38) and in item-level analysis (15/21, 71% vs 40%; P=.04; φ=0.64).

Conclusions: The LP format represents a feasible, scalable, and low-threshold approach to fostering clinical reasoning in dental curricula, particularly at the transition to clinical training. While radiology-specific theoretical competencies did not differ between the groups, students consistently rated the LP as more engaging and motivating compared to standard lectures.

背景：播客越来越多地用于卫生专业教育；然而，大多数格式都是异步和非交互式的。在教学基础上，同步实施的牙科课程是稀缺的。目的：本研究旨在设计、实施和评估一种同步的、基于病例的实时播客（LP）作为牙颌面放射学的教学形式。方法：在一项有2个三年级队列（N=41）的对照队列研究中，干预组（IG， N= 21, 51%）在标准教学的基础上每周接受基于案例的LP课程，而对照组（CG， N= 20, 49%）只接受标准教学。课程结束后6个月，采用27项学生评估问卷和开放文本回答来评估可接受性。课程结束后立即进行21项放射学知识测试，6个月后进行15项跨学科临床应用测试。结果：主要结果是学生报告的LP格式的可接受性。在学生评价问卷中，学生对它的评价很高（平均10分：结构9.76，互动性9.62，跨学科相关性9.55）。对定性反馈进行评估，突出动机、真实性和讨论质量。在放射学知识测试中，组间差异无统计学意义（IG: n= 21,51% vs CG: n= 20,49%； P= 0.37）。在跨学科临床应用测试中，IG在修复牙科（中位数5，IQR 4-5 vs中位数4，IQR 3-5； P= 0.02; r=0.38）和项目水平分析（15/21,71% vs 40%； P= 0.04; φ=0.64）中优于CG。结论：LP格式代表了一种可行的、可扩展的、低门槛的方法来培养牙科课程中的临床推理，特别是在过渡到临床培训时。虽然两组之间的放射学理论能力没有差异，但与标准讲座相比，学生们一致认为LP更吸引人，更有动力。

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引用次数: 0

Training the Digital Clinician by Evaluating Health Education and Curriculum Integration New Zealand Psychology and Psychiatry Programs: Mixed Methods Study. 通过评估健康教育和课程整合来培训数字临床医生新西兰心理学和精神病学课程：混合方法研究。

IF 3.2 Q1 EDUCATION, SCIENTIFIC DISCIPLINES

JMIR Medical Education

Pub Date : 2025-12-31 DOI: 10.2196/72777

Catherine Rawnsley, Karolina Stasiak

Background: The importance of digital health education is widely recognized; however, structural and knowledge deficits hinder its effective integration into training and on-the-job upskilling programs. Tackling these challenges will equip clinicians to navigate the fast-evolving digital mental health landscape confidently.

Objective: This study aims to investigate the prevalence of digital health education and training needs for New Zealand mental health clinicians and trainees, including how psychology and psychiatry teaching programs are including eHealth and digital mental health tools in their curriculums.

Methods: A mixed method study was conducted between August 2021 and February 2022: (1) a survey of mental health clinicians and trainees investigating existing and desired training in digital mental health tools, (2) follow-up in-depth one-on-one interviews with a subsample of survey participants, and (3) in-depth one-on-one interviews with educators (program or curriculum coordinators) within psychology and psychiatry training programs.

Results: The study comprised a survey of 118 clinicians, follow-up interviews with 17 clinicians, and interviews with 4 program directors of relevant training programs. The survey results revealed that 75% (n=88) of the clinicians had not received formal digital health training, yet 69% (n=81) had engaged in self-directed learning. Interest in further training was strong, with 83% (n=98) expressing moderate-to-high interest. Two key themes emerged from the clinician interviews: (1) openness to upskilling, reflecting a willingness to learn, and (2) barriers of time and leadership, highlighting challenges in accessing training due to workloads and limited institutional support. From the program director interviews, three themes were identified: (1) curriculum overload, reflecting difficulties incorporating new content into already crowded programs; (2) uncertainty and inconsistency, with educators unsure about the scope and delivery of digital mental health education; and (3) growth and future potential, highlighting optimism about integrating digital health training into curricula.

Conclusions: The findings reveal a pressing gap in formal digital health training for clinicians despite widespread interest and enthusiasm for upskilling. Key barriers-time constraints, limited institutional leadership, and a lack of educator expertise-are slowing progress.

背景：数字化健康教育的重要性已得到广泛认可；然而，结构性和知识缺陷阻碍了其有效融入培训和在职技能提升计划。应对这些挑战将使临床医生能够自信地驾驭快速发展的数字心理健康领域。目的：本研究旨在调查数字健康教育的流行程度和新西兰心理健康临床医生和学员的培训需求，包括心理学和精神病学教学计划如何在其课程中包括电子健康和数字心理健康工具。方法：在2021年8月至2022年2月期间进行了一项混合方法研究：(1)对心理健康临床医生和受训人员进行调查，调查数字心理健康工具的现有和期望培训，(2)对调查参与者的子样本进行深入的一对一访谈，以及(3)对心理学和精神病学培训计划中的教育工作者（项目或课程协调员）进行深入的一对一访谈。结果：本研究包括对118名临床医生的调查，对17名临床医生的随访访谈，以及对4名相关培训项目的项目主任的访谈。调查结果显示，75% （n=88）的临床医生没有接受过正式的数字健康培训，而69% （n=81）的临床医生参与了自主学习。对进一步培训的兴趣很强，83% （n=98）表达了中等到高度的兴趣。临床医生访谈中出现了两个关键主题：(1)对提高技能的开放态度，反映了学习的意愿；(2)时间和领导的障碍，突出了由于工作量和有限的机构支持而在获得培训方面面临的挑战。从项目主管的采访中，我们发现了三个主题：(1)课程过载，反映出在已经拥挤的项目中融入新内容的困难；(2)不确定性和不一致性，教育者对数字心理健康教育的范围和交付不确定；(3)增长和未来潜力，强调了将数字健康培训纳入课程的乐观态度。结论：研究结果表明，尽管人们对提高技能有广泛的兴趣和热情，但临床医生在正式的数字健康培训方面存在紧迫的差距。主要的障碍——时间限制、有限的机构领导和缺乏教育专业知识——正在减缓进展。

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引用次数: 0

AI in Psychiatric Education and Training From 2016 to 2024: Scoping Review of Trends. 2016年至2024年精神病学教育和培训中的人工智能：趋势的范围审查。

IF 3.2 Q1 EDUCATION, SCIENTIFIC DISCIPLINES

JMIR Medical Education

Pub Date : 2025-12-31 DOI: 10.2196/81517

Michael James Weightman, Anna Chur-Hansen, Scott Richard Clark

Background: Artificial intelligence (AI) is rapidly changing both clinical psychiatry and the education of medical professionals. However, little is currently known about how AI is being discussed in the education and training of psychiatry for medical students and doctors around the world.

Objective: This paper aims to provide a snapshot of the available data on this subject as of 2024. A deliberately broad definition of AI was adopted to capture the widest range of relevant literature and applications, including machine learning, natural language processing, and generative AI tools.

Methods: A scoping review was conducted using both peer-reviewed publications from PubMed, Embase, PsycINFO, and Scopus databases, and gray literature sources. The criterion for inclusion was a description of how AI could be applied to education or training in psychiatry.

Results: A total of 26 records published between 2016 and 2024 were included. The key themes identified were (1) the imperative for an AI curriculum for students or doctors training in psychiatry, (2) uses of AI to develop educational resources, (3) uses of AI to develop clinical skills, (4) uses of AI for assessments, (5) academic integrity or ethical considerations surrounding the use of AI, and (6) tensions relating to competing priorities and directions.

Conclusions: Although a nascent field, it is clear that AI will increasingly impact assessment, clinical skills training, and the development of teaching resources in psychiatry. Training curricula will need to reflect the new knowledge and skills required for future clinical practice. Educators will need to be mindful of academic integrity risks and to emphasize development of critical thinking skills. Attitudes of psychiatrists toward the rise of AI in training remain underexplored.

背景：人工智能（AI）正在迅速改变临床精神病学和医学专业人员的教育。然而，目前对世界各地医科学生和医生在精神病学教育和培训中如何讨论人工智能知之甚少。目的：本文旨在提供截至2024年这一主题的可用数据的快照。人工智能的定义被刻意宽泛，以涵盖最广泛的相关文献和应用，包括机器学习、自然语言处理和生成式人工智能工具。方法：使用PubMed、Embase、PsycINFO和Scopus数据库的同行评议出版物以及灰色文献来源进行范围审查。纳入的标准是描述如何将人工智能应用于精神病学的教育或培训。结果：共纳入2016 - 2024年间发表的26篇文献。确定的关键主题是(1)为精神病学学生或医生培训人工智能课程的必要性，(2)使用人工智能开发教育资源，(3)使用人工智能开发临床技能，(4)使用人工智能进行评估，(5)围绕人工智能使用的学术诚信或道德考虑，以及(6)与竞争优先级和方向相关的紧张关系。结论：虽然人工智能是一个新兴领域，但很明显，人工智能将越来越多地影响精神病学的评估、临床技能培训和教学资源的开发。培训课程需要反映未来临床实践所需的新知识和技能。教育工作者需要注意学术诚信风险，并强调批判性思维技能的发展。精神科医生对人工智能在培训中的兴起的态度仍未得到充分探讨。

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引用次数: 0

Evaluation of Few-Shot AI-Generated Feedback on Case Reports in Physical Therapy Education: Mixed Methods Study. 人工智能在物理治疗教学中对病例报告的反馈评价：混合方法研究。

IF 3.2 Q1 EDUCATION, SCIENTIFIC DISCIPLINES

JMIR Medical Education

Pub Date : 2025-12-30 DOI: 10.2196/85614

Hisaya Sudo, Yoko Noborimoto, Jun Takahashi

Background: While artificial intelligence (AI)-generated feedback offers significant potential to overcome constraints on faculty time and resources associated with providing personalized feedback, its perceived usefulness can be undermined by algorithm aversion. In-context learning, particularly the few-shot approach, has emerged as a promising paradigm for enhancing AI performance. However, there is limited research investigating its usefulness, especially in health profession education.Objective: This study aimed to compare the quality of AI-generated formative feedback from 2 settings, feedback generated in a zero-shot setting (hereafter, "zero-shot feedback") and feedback generated in a few-shot setting (hereafter, "few-shot feedback"), using a mixed methods approach in Japanese physical therapy education. Additionally, we examined the effect of algorithm aversion on these 2 feedback types.Methods: A mixed methods study was conducted with 35 fourth-year physical therapy students (mean age 21.4, SD 0.7 years). Zero-shot feedback was created using Gemini 2.5 Pro with default settings, whereas few-shot feedback was generated by providing the same model with 9 teacher-created examples. The participants compared the quality of both feedback types using 3 methods: a direct preference question, the Feedback Perceptions Questionnaire (FPQ), and focus group interviews. Quantitative comparisons of FPQ scores were performed using the Wilcoxon signed rank test. To investigate algorithm aversion, the study examined how student perceptions changed before and after disclosure of the feedback's identity.Results: Most students (26/35, 74%) preferred few-shot feedback over zero-shot feedback in terms of overall usefulness, although no significant difference was found between the 2 feedback types for the total FPQ score (P=.22). On the specific FPQ scales, few-shot feedback scored significantly higher than zero-shot feedback on fairness across all 3 items: "satisfied" (P=.02; r=0.407), "fair" (P=.04; r=0.341), and "justified" (P=.02; r=0.392). It also scored significantly higher on 1 item of the usefulness scale ("useful"; P=.02; r=0.401) and 1 item of the willingness scale ("invest a lot of effort"; P=.02; r=0.394). In contrast, zero-shot feedback scored significantly higher on the affect scale across 2 items: "successful" (P=.03; r=0.365) and "angry" (P=.008; r=0.443). Regarding algorithm aversion, evaluations for zero-shot feedback became more negative for 83% (15/18) of the items after identity disclosure, whereas positive perceptions of few-shot feedback were maintained or increased. Qualitative analysis revealed that students valued zero-shot feedback for its encouraging tone, whereas few-shot feedback was appreciated for its contextual understanding and concrete guidance for improvement.Conclusions: Japanese physical therapy students perce

背景：虽然人工智能（AI）生成的反馈提供了巨大的潜力，可以克服与提供个性化反馈相关的教师时间和资源的限制，但其感知的有用性可能会被算法厌恶所破坏。上下文学习，特别是少镜头方法，已经成为提高人工智能性能的一个有前途的范例。然而，调查其有用性的研究有限，特别是在卫生专业教育中。目的：本研究旨在比较人工智能在两种情况下生成的形成性反馈的质量，即零镜头环境下生成的反馈（以下简称“零镜头反馈”）和少镜头环境下生成的反馈（以下简称“少镜头反馈”），采用混合方法在日本物理治疗教育中进行。此外，我们还研究了算法厌恶对这两种反馈类型的影响。方法：采用混合方法对35名四年级物理治疗学生（平均年龄21.4岁，SD 0.7岁）进行研究。零镜头反馈是使用默认设置的Gemini 2.5 Pro创建的，而少镜头反馈是通过提供相同的模型和9个教师创建的示例来生成的。参与者使用3种方法比较两种反馈类型的质量：直接偏好问题，反馈感知问卷（FPQ）和焦点小组访谈。FPQ分数的定量比较采用Wilcoxon符号秩检验。为了调查对算法的厌恶，该研究调查了学生在披露反馈身份之前和之后的看法变化。结果：就整体有用性而言，大多数学生（26/35,74%）更喜欢少镜头反馈而不是零镜头反馈，尽管两种反馈类型在FPQ总分上没有显著差异（P= 0.22）。在特定的FPQ量表上，在“满意”（P= 0.02; r=0.407）、“公平”（P= 0.04; r=0.341）和“合理”（P= 0.02; r=0.392）这三个项目上，“少射”反馈的公平性得分明显高于“零射”反馈。在有用性量表（“有用”，P= 0.02; r=0.401）和意愿量表（“投入大量努力”，P= 0.02; r=0.394）的1项得分也显著较高。相比之下，零反馈在“成功”（P= 0.03; r=0.365）和“愤怒”（P= 0.008; r=0.443）这两个项目的情感量表上得分明显更高。在算法厌恶方面，身份披露后83%（15/18）的项目对零次反馈的评价变得更加负面，而对少次反馈的正面评价保持或增加。定性分析显示，学生们对零镜头反馈的鼓励语气非常重视，而对少镜头反馈的上下文理解和具体的改进指导则很受欢迎。结论：日本物理治疗学生对病例报告的反馈比零反馈更有好感。这种少量的人工智能模型显示出抵抗算法厌恶的潜力，并可作为有效的教育工具，支持自主写作，促进临床推理反思，培养高级思维技能。

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引用次数: 0

Fostering Multidisciplinary Collaboration in Artificial Intelligence and Machine Learning Education: Tutorial Based on the AI-READI Bootcamp. 促进人工智能和机器学习教育中的多学科合作：基于AI-READI训练营的教程。

IF 3.2 Q1 EDUCATION, SCIENTIFIC DISCIPLINES

JMIR Medical Education

Pub Date : 2025-12-29 DOI: 10.2196/83154

Taiki W Nishihara, Fritz Gerald P Kalaw, Adelle Engmann, Aya Motoyoshi, Paapa Mensah-Kane, Deepa Gupta, Victoria Patronilo, Linda M Zangwill, Shahin Hallaj, Amirhossein Panahi, Garrison W Cottrell, Bradley Voytek, Virginia R de Sa, Sally L Baxter

Background: The integration of artificial intelligence (AI) and machine learning (ML) into biomedical research requires a workforce fluent in both computational methods and clinical applications. Structured, interdisciplinary training opportunities remain limited, creating a gap between data scientists and clinicians. The National Institutes of Health's Bridge to Artificial Intelligence (Bridge2AI) initiative launched the Artificial Intelligence-Ready and Exploratory Atlas for Diabetes Insights (AI-READI) data generation project to address this gap. AI-READI is creating a multimodal, FAIR (findable, accessible, interoperable, and reusable) dataset-including ophthalmic imaging, physiologic measurements, wearable sensor data, and survey responses-from approximately 4000 participants with or at risk for type 2 diabetes. In parallel, AI-READI established a year-long mentored research program that begins with a 2-week immersive summer bootcamp to provide foundational AI/ML skills grounded in domain-relevant biomedical data.Objective: To describe the design, iterative refinement, and outcomes of the AI-READI Bootcamp, and to share lessons for creating future multidisciplinary AI/ML training programs in biomedical research.Methods: Held annually at the University of California San Diego, the bootcamp combines 80 hours of lectures, coding sessions, and small-group mentorship. Year 1 introduced Python programming, classical ML techniques (eg, logistic regression, convolutional neural networks), and data science methods, such as principal component analysis and clustering, using public datasets. In Year 2, the curriculum was refined based on structured participant feedback-reducing cohort size to increase individualized mentorship, integrating the AI-READI dataset (including retinal images and structured clinical variables), and adding modules on large language models and FAIR data principles. Participant characteristics and satisfaction were assessed through standardized pre- and postbootcamp surveys, and qualitative feedback was analyzed thematically by independent coders.Results: Seventeen participants attended Year 1 and 7 attended Year 2, with an instructor-to-student ratio of approximately 1:2 in the latter. Across both years, postbootcamp evaluations indicated high satisfaction, with Year 2 participants reporting improved experiences due to smaller cohorts, earlier integration of the AI-READI dataset, and greater emphasis on applied learning. In Year 2, mean scores for instructor effectiveness, staff support, and overall enjoyment were perfect (5.00/5.00). Qualitative feedback emphasized the value of working with domain-relevant, multimodal datasets; the benefits of peer collaboration; and the applicability of skills to structured research projects during the subsequent internship year.Conclusions: The AI-READI Bootcamp illustrates how

背景：将人工智能（AI）和机器学习（ML）整合到生物医学研究中，需要在计算方法和临床应用方面都很熟练的工作人员。结构化的、跨学科的培训机会仍然有限，这造成了数据科学家和临床医生之间的差距。美国国立卫生研究院的人工智能之桥（Bridge2AI）倡议启动了糖尿病洞察人工智能准备和探索性地图集（AI-READI）数据生成项目，以解决这一差距。AI-READI正在创建一个多模式、FAIR（可查找、可访问、可互操作和可重复使用）数据集，包括眼科成像、生理测量、可穿戴传感器数据和调查反馈，来自大约4000名患有或有2型糖尿病风险的参与者。与此同时，AI- readi建立了一个为期一年的指导研究项目，首先是为期两周的沉浸式夏季训练营，提供基于领域相关生物医学数据的基础AI/ML技能。目的：描述AI- readi训练营的设计、迭代改进和结果，并分享在生物医学研究中创建未来多学科AI/ML培训计划的经验教训。方法：该训练营每年在加州大学圣地亚哥分校举行，包括80小时的讲座、编程课程和小组指导。第一年介绍了Python编程、经典ML技术（如逻辑回归、卷积神经网络）和数据科学方法，如主成分分析和聚类，使用公共数据集。在第二学年，课程在结构化参与者反馈的基础上进行了细化，减少了队列规模以增加个性化指导，整合了AI-READI数据集（包括视网膜图像和结构化临床变量），并增加了基于大型语言模型和FAIR数据原理的模块。通过标准化的训练营前后调查评估参与者的特征和满意度，并由独立编码人员对定性反馈进行主题分析。结果：17名参与者参加了一年级，7名参加了二年级，后者的师生比例约为1:2。在这两年中，训练营后的评估显示出很高的满意度，由于更小的队列，更早地整合AI-READI数据集，以及更重视应用学习，二年级参与者报告了改善的体验。在第二年，教师效能、员工支持和整体享受的平均得分是完美的（5.00/5.00）。定性反馈强调了处理领域相关、多模态数据集的价值；同侪协作的好处；以及在接下来的实习年度中，技能对结构化研究项目的适用性。结论：AI- readi训练营说明了在纵向指导研究项目中嵌入反馈驱动的多学科培训如何能够在生物医学人工智能的技术和临床专业知识之间建立桥梁。核心要素——多样化的学员队伍、生物医学数据集的应用学习和持续的指导——为卫生专业人员为不断发展的人工智能/机器学习领域做好准备提供了一个可复制的模型。未来的迭代将包含额外的预训练营入门模块，客观技能评估，以及研究参与和生产力的长期跟踪。

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引用次数: 0

Effectiveness of a 5G Local Area Network-Based Digital Microscopy Interactive System: Quasi-Experimental Design. 基于5G局域网的数字显微镜交互系统的有效性：准实验设计。

IF 3.2 Q1 EDUCATION, SCIENTIFIC DISCIPLINES

JMIR Medical Education

Pub Date : 2025-12-24 DOI: 10.2196/70256

Jie Xu, Jihong Sha, Song Jia, Jiao Li, Lei Xu, Zhihua Shao

Background: Technological innovation is reshaping the landscape of medical education, bringing revolutionary changes to traditional teaching methods. In this context, the upgrade of the teaching model for microscopy, as one of the core skills in medical education, is particularly important. Proficiency in microscope operation not only affects medical students' pathology diagnosis abilities but also directly impacts the precision of surgical procedures and laboratory analysis skills. However, current microscopy pedagogy faces dual challenges: on one hand, traditional teaching lacks real-time image sharing capabilities, severely limiting the effectiveness of immediate instructor guidance; on the other hand, students find it difficult to independently identify technical flaws in their operations, leading to inefficient skill acquisition. Although whole-slide imaging-based microscopy system technology has partially addressed the issue of image visualization, it cannot replicate the tactile feedback and physical interaction experience of the real world. The breakthrough development of 5G communication technology-with its ultrahigh transmission speed and ultralow latency-provides an innovative solution to this teaching challenge. Leveraging this technological advantage, Tongji University's biology laboratory has pioneered the deployment of a 5G local area network (LAN)-supported digital interactive microscopy system, creating a new model for microscopy education.Objective: This study aims to investigate the efficacy of an innovative 5G LAN-powered interactive digital microscopy system in enhancing microscopy training efficiency, evaluated through medical students' academic performance and learning experience.Methods: Using a quasi-experimental design, we quantify system effectiveness via academic performance metrics and learning experience dimensions. A total of 39 students enrolled in the biology course were randomly assigned to 2 groups: one using traditional optical microscopes (control) and the other using the digital microscopy interactive system (DMIS). Their academic performance was evaluated through a knowledge test and 3 laboratory reports. A 5-point Likert-scale questionnaire was used to gather feedback on students' learning experiences. In addition, the DMIS group was required to evaluate the specific functions of the system.Results: In the knowledge test, no statistical difference was found between the 2 groups; however, the DMIS group scored significantly higher in Lecture 2 (P<.05). In the laboratory reports, the DMIS group performed significantly better than the control group (mean 90.33, SD 2.63 vs mean 80.53, SD 3.52, P<.001). Questionnaire results indicated that the DMIS group has a positive evaluation of the system and expressed greater confidence in its future application. For the evaluation of the laboratory lectures, the DMIS group received

背景：技术创新正在重塑医学教育的格局，给传统的教学方法带来革命性的变化。在此背景下，作为医学教育核心技能之一的显微术教学模式的升级就显得尤为重要。能否熟练掌握显微镜操作不仅影响医学生的病理诊断能力，而且直接影响到外科手术的精确性和实验室分析技能。然而，当前的显微学教学面临着双重挑战：一方面，传统教学缺乏实时图像共享能力，严重限制了教师即时指导的有效性；另一方面，学生很难独立识别操作中的技术缺陷，导致技能习得效率低下。尽管基于全玻片成像的显微系统技术已经部分解决了图像可视化问题，但它不能复制真实世界的触觉反馈和物理交互体验。5G通信技术的突破性发展，以其超高的传输速度和超低的延迟，为这一教学挑战提供了创新的解决方案。利用这一技术优势，同济大学生物实验室率先部署了支持5G局域网（LAN）的数字交互式显微镜系统，创造了显微镜教育的新模式。目的：通过对医学生学习成绩和学习体验的评价，探讨创新的5G局域网交互式数字显微镜系统在提高显微镜培训效率方面的效果。方法：采用准实验设计，通过学习成绩指标和学习经验维度量化系统有效性。39名生物学课程的学生被随机分为两组：一组使用传统光学显微镜（对照组），另一组使用数字显微镜交互系统（DMIS）。他们的学习成绩通过一项知识测试和三份实验室报告进行评估。采用李克特5分制问卷收集学生学习经验的反馈。此外，还要求DMIS小组评价该系统的具体功能。结果：在知识测试中，两组间差异无统计学意义；结论：总体而言，数码显微镜互动系统增强了学生的学习体验，提高了他们的学习成绩。它提供了各种互动功能，方便组织教学活动，促进课堂上的即时反馈。因此，它是一种很有前途的显微镜实验教学工具。

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引用次数: 0

Using AI-Based Virtual Simulated Patients for Training in Psychopathological Interviewing: Cross-Sectional Observational Study. 使用基于人工智能的虚拟模拟患者进行精神病理学访谈训练：横断面观察研究。

IF 3.2 Q1 EDUCATION, SCIENTIFIC DISCIPLINES

JMIR Medical Education

Pub Date : 2025-12-23 DOI: 10.2196/78857

Daniel García-Torres, César Fernández, José Joaquín Mira, Alexandra Morales, María Asunción Vicente

Background: Virtual simulated patients (VSPs) powered by generative artificial intelligence (GAI) offer a promising tool for training clinical interviewing skills; yet, little is known about how different system- and user-level variables shape students' perceptions of these interactions.Objective: We aim to study psychology students' perceptions of GAI-driven VSPs and examine how demographic factors, system parameters, and interaction characteristics influence such perceptions.Methods: We conducted a total of 1832 recorded interactions involving 156 psychology students with 13 GAI-generated VSPs configured with varying temperature settings (0.1, 0.5, 0.9). For each student, we collected age and sex; for each interview, we recorded interview length (total number of question-answer turns), number of connectivity failures, the specific VSP consulted, and the model temperature. After every interview, students provided a 1-10 global rating and open-ended comments regarding strengths and areas for improvement. At the end of the training sequence, they also reported perceived improvement in diagnostic ability. Statistical analyses assessed the influence of different variables on global ratings: demographics, interaction-level data, and GAI temperature setting. Sentiment analysis was conducted to evaluate the VSPs' clinical realism.Results: Statistical analysis showed that female students rated the tool significantly higher (mean rating 9.25/10) than male students (mean rating 8.94/10; Kruskal-Wallis test, H=8.7; P=.003). On the other side, no significant correlation was found between global rating and age (r=0.02, 95% CI -0.03 to 0.06; P=.42), interview length (r=0.04, 95% CI -0.2 to 0.10; P=.18), or frequency of participation (Kruskal-Wallis test, H=4.62; P=.20). A moderate negative correlation emerged between connectivity failures and ratings (r=-0.26, 95% CI -0.41 to -0.10; P=.002). Temperature settings significantly influenced ratings (Kruskal-Wallis test, H=6.93; P=.03; η²=0.02), with higher scores at temperature 0.9 compared with 0.1 (Dunn's test, P=.04). Concerning learning outcomes, self-perceived improvement in diagnostic ability was reported by 94% (94/100) of students; however, final practical examination scores (mean 6.67, SD 1.42) did not differ significantly from those of the previous cohort without VSP training (mean 6.42, SD 1.56). Sentiment analysis indicated predominantly negative sentiment in GAI responses (median negativity 0.8903, IQR 0.306-0.961), consistent with clinical realism.Conclusions: GAI-driven VSPs were well-received by psychology students, with student gender and system-level variables (particularly temperature settings and connection stability) shaping user evaluations. Although participants perceived the training as beneficial for their diagnostic skills, objective examination performance did not signific

背景：由生成式人工智能（GAI）驱动的虚拟模拟患者（vsp）为临床访谈技能培训提供了一个很有前途的工具；然而，对于不同的系统和用户层面的变量如何影响学生对这些互动的看法，我们知之甚少。目的：研究心理学专业学生对人工智能驱动vsp的看法，并研究人口因素、系统参数和交互特征如何影响这种看法。方法：我们对156名心理学学生进行了1832次互动记录，他们使用了13个由ai生成的vsp，设置了不同的温度设置（0.1,0.5,0.9）。我们收集了每个学生的年龄和性别；对于每次访谈，我们记录了访谈长度（问答回合总数）、连接失败次数、咨询的特定VSP和模型温度。每次面试结束后，学生们提供1-10分的全球评分，并就自己的优势和需要改进的地方发表开放式评论。在训练结束时，他们也报告了诊断能力的改善。统计分析评估了不同变量对全球评级的影响：人口统计、互动水平数据和GAI温度设置。采用情绪分析评价vsp的临床现实性。结果：统计分析显示，女生对该工具的评分（平均评分9.25/10）显著高于男生（平均评分8.94/10）；Kruskal-Wallis检验，H=8.7； P= 0.003)。另一方面，总体评分与年龄（r=0.02, 95% CI -0.03至0.06；P= 0.42）、访谈长度（r=0.04, 95% CI -0.2至0.10；P= 0.18）或参与频率（Kruskal-Wallis检验，H=4.62； P= 0.20）之间无显著相关性。连接失败和评分之间存在适度的负相关（r=-0.26, 95% CI -0.41至-0.10；P= 0.002）。温度设置显著影响评分（Kruskal-Wallis测试，H=6.93； P= 0.03; η²=0.02），温度为0.9的评分高于温度为0.1的评分（Dunn测试，P= 0.04）。在学习成果方面，94%（94/100）的学生自我感知诊断能力有所提高；然而，最后的实践考试成绩（平均6.67,SD 1.42）与未接受VSP训练的队列（平均6.42,SD 1.56）没有显著差异。情绪分析显示GAI反应以消极情绪为主（中位数负性0.8903，IQR为0.306-0.961），与临床现实相符。结论：ai驱动的vsp受到心理学学生的欢迎，学生的性别和系统级变量（特别是温度设置和连接稳定性）影响了用户的评价。尽管参与者认为培训对他们的诊断技能有益，但客观考试成绩与前一队列没有显着差异。然而，缺乏随机化限制了所得结果的泛化，需要进一步的实验。

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引用次数: 0

Trends in the Japanese National Medical Licensing Examination: Cross-Sectional Study. 日本全国医师执照考试趋势：横断面研究。

IF 3.2 Q1 EDUCATION, SCIENTIFIC DISCIPLINES

JMIR Medical Education

Pub Date : 2025-12-23 DOI: 10.2196/78214

Yuki Morimoto, Kiyoshi Shikino, Yukihiro Nomura, Shoichi Ito

Background: The Japanese National Medical Licensing Examination (NMLE) is mandatory for all medical graduates seeking to become licensed physicians in Japan. Given the cultural emphasis on summative assessment, the NMLE has had a significant impact on Japanese medical education. Although the NMLE Content Guidelines have been revised approximately every five years over the last 2 decades, objective literature analyzing how the examination itself has evolved is absent.Objective: To provide a holistic view of the trends of the actual examination over time, this study used a combined rule-based and data-driven approach. We primarily focused on classifying the items according to the perspectives outlined in the NMLE Content Guidelines, complementing this approach with a natural language processing technique called topic modeling to identify latent topics.Methods: We collected publicly available NMLE data for 2001-2024. Six examination iterations (2880 items) were manually classified from 3 perspectives (level, content, and taxonomy) based on pre-established rules derived from the guidelines. Temporal trends within each classification were evaluated using the Cochran-Armitage test. Additionally, we conducted topic modeling for all 24 examination iterations (11,540 items) using the bidirectional encoder representations from transformers-based topic modeling framework. Temporal trends were traced using linear regression models of topic frequencies to identify topics growing in prominence.Results: In the level classification, the proportion of items addressing common or emergent diseases increased from 60% (115/193) to 76% (111/147; P<.001). In the content classification, the proportion of items assessing knowledge of pathophysiology decreased from 52% (237/459) to 33% (98/293; P<.001), whereas the proportion assessing practical knowledge of primary emergency care increased from 21% (95/459) to 29% (84/293; P<.001). In the taxonomy classification, the proportion of items that could be answered solely through simple recall of knowledge decreased from 51% (279/550) to 30% (118/400; P<.001), while the proportion assessing advanced analytical skills, such as interpreting and evaluating the meaning of each answer choice according to the given context, increased from 4% (21/550) to 19% (75/400; P<.001). Topic modeling identified 25 distinct topics, of which 10 exhibited an increasing trend. Non-organ-specific topics with notable increases included "comprehensive clinical items," "accountability in medical practice and patients' rights," "care, daily living support, and community health care," and "infection control and safety management in basic clinical procedures."Conclusions: This study identified significant shifts in the Japanese NMLE over the past 2 decades, suggesting that Japanese undergraduate medical education is evolving to place greate

背景：日本国家医疗执照考试（NMLE）是强制性的所有医学毕业生寻求成为日本执业医师。鉴于文化上对总结性评估的重视，NMLE对日本医学教育产生了重大影响。虽然NMLE内容指南在过去20年中大约每五年修订一次，但缺乏客观的文献分析考试本身是如何演变的。目的：为了全面了解实际检查的趋势，本研究采用了基于规则和数据驱动的结合方法。我们主要关注根据NMLE内容指南中概述的观点对条目进行分类，并使用称为主题建模的自然语言处理技术对该方法进行补充，以识别潜在主题。方法：我们收集了2001-2024年公开的NMLE数据。基于源自指南的预先建立的规则，从3个角度（级别、内容和分类法）手动对6个检查迭代（2880个项目）进行分类。使用Cochran-Armitage检验评估每个分类的时间趋势。此外，我们使用来自基于转换器的主题建模框架的双向编码器表示对所有24个检查迭代（11,540个项目）进行了主题建模。使用主题频率的线性回归模型追踪时间趋势，以确定日益突出的主题。结果：在水平分类中，常见病和突发疾病的项目比例从60%（115/193）增加到76%(111/147)；结论：本研究发现日本NMLE在过去20年中发生了显著变化，表明日本本科医学教育正在向更重视实际问题解决能力而不是死记硬背的方向发展。这项研究还发现了潜在的话题，显示出越来越突出，可能反映了潜在的社会条件。

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引用次数: 0

Correction: Comparing the Perceived Realism and Adequacy of Venipuncture Training on an in-House Developed 3D-Printed Arm With a Commercially Available Arm: Randomized, Single-Blind, Cross-Over Study. 更正：比较内部开发的3d打印手臂与市售手臂上静脉穿刺训练的感知真实性和充分性：随机，单盲，交叉研究。

IF 3.2 Q1 EDUCATION, SCIENTIFIC DISCIPLINES

JMIR Medical Education

Pub Date : 2025-12-22 DOI: 10.2196/89670

Susan Gijsbertje Brouwer de Koning, Amy Hofman, Sonja Gerber, Vera Lagerburg, Michelle van den Boorn

引用次数: 0