JMIR Medical Education最新文献

{"title":"Chatbots' Role in Generating Single Best Answer Questions for Undergraduate Medical Student Assessment: Comparative Analysis.","authors":"Enjy Abouzeid, Rita Wassef, Ayesha Jawwad, Patricia Harris","doi":"10.2196/69521","DOIUrl":"https://doi.org/10.2196/69521","url":null,"abstract":"<p><strong>Background: </strong>Programmatic assessment supports flexible learning and individual progression but challenges educators to develop frequent assessments reflecting different competencies. The continuous creation of large volumes of assessment items, in a consistent format and comparatively restricted time, is laborious. The application of technological innovations, including artificial intelligence (AI), has been tried to address this challenge. A major concern raised is the validity of the information produced by AI tools, and if not properly verified, it can produce inaccurate and therefore inappropriate assessments.</p><p><strong>Objective: </strong>This study was designed to examine the content validity and consistency of different AI chatbots in creating single best answer (SBA) questions, a refined format of multiple choice questions better suited to assess higher levels of knowledge, for undergraduate medical students.</p><p><strong>Methods: </strong>This study followed 3 steps. First, 3 researchers used a unified prompt script to generate 10 SBA questions across 4 chatbot platforms. Second, assessors evaluated the chatbot outputs for consistency by identifying similarities and differences between users and across chatbots. With 3 assessors and 10 learning objectives, the maximum possible score for any individual chatbot was 30. Third, 7 assessors internally moderated the questions using a rating scale developed by the research team to evaluate scientific accuracy and educational quality.</p><p><strong>Results: </strong>In response to the prompts, all chatbots generated 10 questions each, except Bing, which failed to respond to 1 prompt. ChatGPT-4 exhibited the highest variation in question generation but did not fully satisfy the \"cover test.\" Gemini performed well across most evaluation criteria, except for item balance, and relied heavily on the vignette for answers but showed a preference for one answer option. Bing scored low in most evaluation areas but generated appropriately structured lead-in questions. SBA questions from GPT-3.5, Gemini, and ChatGPT-4 had similar Item Content Validity Index and Scale Level Content Validity Index values, while the Krippendorff alpha coefficient was low (0.016). Bing performed poorly in content clarity, overall validity, and item construction accuracy. A 2-way ANOVA without replication revealed statistically significant differences among chatbots and domains (P<.05). However, the Tukey-Kramer HSD (honestly significant difference) post hoc test showed no significant pairwise differences between individual chatbots, as all comparisons had P values >.05 and overlapping CIs.</p><p><strong>Conclusions: </strong>AI chatbots can aid the production of questions aligned with learning objectives, and individual chatbots have their own strengths and weaknesses. Nevertheless, all require expert evaluation to ensure their suitability for use. Using AI to generate SBA prompts us to reconsider Bloom","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"11 ","pages":"e69521"},"PeriodicalIF":3.2,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144188234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multidisciplinary Oncology Education Among Postgraduate Trainees: Systematic Review.","authors":"Houman Tahmasebi, Gary Ko, Christine M Lam, Idil Bilgen, Zachary Freeman, Rhea Varghese, Emma Reel, Marina Englesakis, Tulin D Cil","doi":"10.2196/63655","DOIUrl":"https://doi.org/10.2196/63655","url":null,"abstract":"<p><strong>Background: </strong>Understanding the roles and patient management approaches of the entire oncology team is imperative for effective communication and optimal cancer treatment. Currently, there is no standard residency or fellowship curriculum to ensure the delivery of fundamental knowledge and skills associated with oncology specialties with which trainees often collaborate.</p><p><strong>Objective: </strong>This study is a systematic review that aims to evaluate the multidisciplinary oncology education in postgraduate medical training.</p><p><strong>Methods: </strong>A systematic literature search was performed using MEDLINE, Embase, Cochrane Database of Systematic Reviews, Cochrane CENTRAL, APA PsycINFO, and Education Resources Information Center in July 2021. Updates were performed in February 2023 and October 2024. Original studies reporting the effectiveness of multidisciplinary oncology training among residents and fellows were included.</p><p><strong>Results: </strong>A total of 6991 studies were screened and 24 were included. Fifteen studies analyzed gaps in existing multidisciplinary training of residents and fellows from numerous fields, including surgical, medical, and radiation oncology; geriatrics; palliative medicine; radiology; and pathology programs. Trainees reported limited teaching and knowledge of oncology outside of their respective fields and endorsed the need for further multidisciplinary oncology training. The remaining 9 studies assessed the effectiveness of educational interventions, including tumor boards, didactic sessions, clinical rotations, and case-based learning. Trainees reported significant improvements in multidisciplinary oncology knowledge and skills following the interventions.</p><p><strong>Conclusions: </strong>These data suggest postgraduate medical trainees have limited formal multidisciplinary oncology training. Existing educational interventions show promising results in improving trainees' oncology knowledge and skills. There is a need for further research and the development of multidisciplinary oncology curricula for postgraduate medical training programs.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"11 ","pages":"e63655"},"PeriodicalIF":3.2,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144152163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel Blended Learning on Artificial Intelligence for Medical Students: Qualitative Interview Study.","authors":"Zoe S Oftring, Kim Deutsch, Daniel Tolks, Florian Jungmann, Sebastian Kuhn","doi":"10.2196/65220","DOIUrl":"https://doi.org/10.2196/65220","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Artificial intelligence (AI) systems are becoming increasingly relevant in everyday clinical practice, with Food and Drug Administration-approved AI solutions now available in many specialties. This development has far-reaching implications for doctors and the future medical profession, highlighting the need for both practicing physicians and medical students to acquire the knowledge, skills, and attitudes necessary to effectively use and evaluate these technologies. Currently, however, there is limited experience with AI-focused curricular training and continuing education.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This paper first introduces a novel blended learning curriculum including one module on AI for medical students in Germany. Second, this paper presents findings from a qualitative postcourse evaluation of students' knowledge and attitudes toward AI and their overall perception of the course.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Clinical-year medical students can attend a 5-day elective course called \"Medicine in the Digital Age,\" which includes one dedicated AI module alongside 4 others on digital doctor-patient communication; digital health applications and smart devices; telemedicine; and virtual/augmented reality and robotics. After course completion, participants were interviewed in semistructured small group interviews. The interview guide was developed deductively from existing evidence and research questions compiled by our group. A subset of interview questions focused on students' knowledge, skills, and attitudes regarding medical AI, and their overall course assessment. Responses were analyzed using Mayring's qualitative content analysis. This paper reports on the subset of students' statements about their perception and attitudes toward AI and the elective's general evaluation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;We conducted a total of 18 group interviews, in which all 35 (100%) participants (female=11, male=24) from 3 consecutive course runs participated. This produced a total of 214 statements on AI, which were assigned to the 3 main categories \"Areas of Application,\" \"Future Work,\" and \"Critical Reflection.\" The findings indicate that students have a nuanced and differentiated understanding of AI. Additionally, 610 statements concerned the elective's overall assessment, demonstrating great learning benefits and high levels of acceptance of the teaching concept. All 35 students would recommend the elective to peers.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The evaluation demonstrated that the AI module effectively generates competences regarding AI technology, fosters a critical perspective, and prepares medical students to engage with the technology in a differentiated manner. The curriculum is feasible, beneficial, and highly accepted among students, suggesting it could serve as a teaching model for other medical institutions. Given the growing number and impact of medical AI applications, there is a pressing","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"11 ","pages":"e65220"},"PeriodicalIF":3.2,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144152165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Web-Based Continuing Education to Improve New Diagnoses of Alzheimer Disease in Claims Data: Retrospective Case-Control Study.","authors":"Katie Lucero, Thomas Finnegan, Soo Borson","doi":"10.2196/72000","DOIUrl":"10.2196/72000","url":null,"abstract":"<p><strong>Background: </strong>Alzheimer disease (AD) presents significant challenges to health care systems worldwide. Early and accurate diagnosis of AD is crucial for effective management and care to enable timely treatment interventions that can preserve cognitive function and improve patient quality of life. However, there are often significant delays in diagnosis. Continuing medical education (CME) has enhanced physician knowledge and confidence in various medical fields, including AD. Notably, web-based CME has been shown to positively influence physician confidence, which can lead to changes in practice and increased adoption of evidence-based treatment selection.</p><p><strong>Objective: </strong>This study investigated the impact of a targeted, web-based CME intervention on health care providers' confidence, competence, and real-world outcomes in diagnosing early AD.</p><p><strong>Methods: </strong>The study employed a 2-phase design. Phase I used a pre-post assessment to evaluate immediate changes in knowledge and confidence before and after CME participation. Phase II involved a retrospective, matched case-control study to examine the impact of CME on AD diagnoses in claims data.</p><p><strong>Results: </strong>A 1-way ANOVA showed a significant effect of CME regarding change in the volume of AD diagnoses (F1900=5.50; P=.02). Compared to controls, CME learners were 1.6 times more likely to diagnose AD, resulting in an estimated net increase of 7939 new diagnoses annually. Post-CME confidence was associated with a greater likelihood of diagnosing AD (odds ratio 1.64; 95% CI 0.92-2.92; P=.09; n=219).</p><p><strong>Conclusions: </strong>Web-based CME participation is associated with increased real-world AD diagnoses. Findings offer a mechanism to explain the changes in clinical practice seen as a result of the CME intervention, which improves skills and confidence.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"11 ","pages":"e72000"},"PeriodicalIF":3.2,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12121534/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144121012","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}

{"title":"Assessing ChatGPT's Capability as a New Age Standardized Patient: Qualitative Study.","authors":"Joseph Cross, Tarron Kayalackakom, Raymond E Robinson, Andrea Vaughans, Roopa Sebastian, Ricardo Hood, Courtney Lewis, Sumanth Devaraju, Prasanna Honnavar, Sheetal Naik, Jillwin Joseph, Nikhilesh Anand, Abdalla Mohammed, Asjah Johnson, Eliran Cohen, Teniola Adeniji, Aisling Nnenna Nnaji, Julia Elizabeth George","doi":"10.2196/63353","DOIUrl":"10.2196/63353","url":null,"abstract":"<p><strong>Background: </strong>Standardized patients (SPs) have been crucial in medical education, offering realistic patient interactions to students. Despite their benefits, SP training is resource-intensive and access can be limited. Advances in artificial intelligence (AI), particularly with large language models such as ChatGPT, present new opportunities for virtual SPs, potentially addressing these limitations.</p><p><strong>Objectives: </strong>This study aims to assess medical students' perceptions and experiences of using ChatGPT as an SP and to evaluate ChatGPT's effectiveness in performing as a virtual SP in a medical school setting.</p><p><strong>Methods: </strong>This qualitative study, approved by the American University of Antigua Institutional Review Board, involved 9 students (5 females and 4 males, aged 22-48 years) from the American University of Antigua College of Medicine. Students were observed during a live role-play, interacting with ChatGPT as an SP using a predetermined prompt. A structured 15-question survey was administered before and after the interaction. Thematic analysis was conducted on the transcribed and coded responses, with inductive category formation.</p><p><strong>Results: </strong>Thematic analysis identified key themes preinteraction including technology limitations (eg, prompt engineering difficulties), learning efficacy (eg, potential for personalized learning and reduced interview stress), verisimilitude (eg, absence of visual cues), and trust (eg, concerns about AI accuracy). Postinteraction, students noted improvements in prompt engineering, some alignment issues (eg, limited responses on sensitive topics), maintained learning efficacy (eg, convenience and repetition), and continued verisimilitude challenges (eg, lack of empathy and nonverbal cues). No significant trust issues were reported postinteraction. Despite some limitations, students found ChatGPT as a valuable supplement to traditional SPs, enhancing practice flexibility and diagnostic skills.</p><p><strong>Conclusions: </strong>ChatGPT can effectively augment traditional SPs in medical education, offering accessible, flexible practice opportunities. However, it cannot fully replace human SPs due to limitations in verisimilitude and prompt engineering challenges. Integrating prompt engineering into medical curricula and continuous advancements in AI are recommended to enhance the use of virtual SPs.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"11 ","pages":"e63353"},"PeriodicalIF":3.2,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12111480/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144112101","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}

{"title":"Exploring Connections Between Mental Health, Burnout, and Academic Factors Among Medical Students at an Iranian University: Cross-Sectional Questionnaire Study.","authors":"Elham Faghihzadeh, Ali Eghtesad, Muhammad Fawad, Xiaolin Xu","doi":"10.2196/58008","DOIUrl":"10.2196/58008","url":null,"abstract":"<p><strong>Background: </strong>Medical students face high levels of burnout and mental health issues during training. Understanding associated factors can inform supportive interventions.</p><p><strong>Objective: </strong>This study aimed to examine burnout, psychological well-being, and related demographics among Iranian medical students.</p><p><strong>Methods: </strong>A cross-sectional survey was conducted among 131 medical students at an Iranian University. The instruments used included the Maslach Burnout Inventory-Student Survey and the Symptom Checklist-90-Revised. Descriptive statistics, multivariate regression, and tests for group differences were used to analyze the data.</p><p><strong>Results: </strong>The MBI-SS subscale scores indicated moderate emotional exhaustion, mean 15.00 (SD 7.08) and academic efficacy, mean 14.98 (SD 6.29), with lower cynicism, mean 10.85 (SD 5.89). The most commonly reported mental health issues were depression and obsessive-compulsive disorder. Poor psychological well-being was associated with higher overall burnout, but no significant gender differences were found. Burnout levels varied by academic year across all Maslach Burnout Inventory-Student Survey domains.</p><p><strong>Conclusions: </strong>Despite their health education, medical students in this study reported significant burnout and mental health distress, with strong associations between the two. These issues may impact student retention and post-graduation practice plans. Supporting well-being during training is critical for positive student and physician outcomes.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"11 ","pages":"e58008"},"PeriodicalIF":3.2,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12097282/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144081184","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}

{"title":"Bridging Gaps in Telemedicine Education in Romania to Support Future Health Care: Scoping Review.","authors":"Mircea Adrian Focsa, Virgil Rotaru, Octavian Andronic, Marius Marginean, Sorin Florescu","doi":"10.2196/66458","DOIUrl":"10.2196/66458","url":null,"abstract":"<p><strong>Background: </strong>Telemedicine is a key element of modern health care, providing remote medical consultations and bridging the gap between patients and health care providers. Despite legislative advancements and pilot programs, the integration of telemedicine education in Romania remains limited. Addressing these educational gaps is essential for preparing current and future medical professionals to effectively use telemedicine technologies.</p><p><strong>Objective: </strong>This study aimed to evaluate the current state of telemedicine education for medical professionals in Romania, focusing on the integration of diagnostic and therapeutic capabilities into medical curricula, identifying the challenges and opportunities, and providing recommendations for improving telemedicine education.</p><p><strong>Methods: </strong>A scoping review was conducted following Arksey and O'Malley's framework. Peer-reviewed articles from 2019 to 2023 were identified using databases such as PubMed and Scopus. Additional gray literature was reviewed to provide a comprehensive understanding of telemedicine education in Romania. Data were thematically analyzed to extract key findings and recommendations.</p><p><strong>Results: </strong>The review identified significant progress in the legislative and infrastructural aspects of telemedicine in Romania, but highlighted gaps in integrating telemedicine education into curricula for medical professionals and other health care practitioners directly involved in telemedicine practices. While some universities have included telemedicine components, dedicated telemedicine courses and hands-on training remain insufficient. Barriers include a lack of infrastructure, digital literacy, and practical exposure to telemedicine technologies.</p><p><strong>Conclusions: </strong>For telemedicine to be effectively integrated into Romania's health care system, medical education must be adapted to include comprehensive telemedicine training. Recommendations include enhancing digital literacy, fostering public-private partnerships, and incorporating telemedicine into undergraduate and continuous professional education programs. These efforts are essential for improving healthcare access and quality through telemedicine.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"11 ","pages":"e66458"},"PeriodicalIF":3.2,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12094530/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144081183","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}

{"title":"Enhancing Access to Neuraxial Ultrasound Phantoms for Medical Education of Pediatric Anesthesia Trainees: Tutorial.","authors":"Leah Webb, Melissa Masaracchia, Kim Strupp","doi":"10.2196/63682","DOIUrl":"10.2196/63682","url":null,"abstract":"<p><strong>Unlabelled: </strong>Opportunities to learn ultrasound-guided/assisted (USGA) neuraxial techniques for pediatric patients are limited, given the inherent high stakes and small margin of error in this population. Simulation is especially valuable in pediatrics because it enhances competency and efficiency, without added risk, when learning new skills, specifically those seen with ultrasound-guided regional anesthetic techniques. However, access to simulation opportunities involving the use of phantom models in medical education is limited due to excessive costs. We describe a process for producing ultrasound phantoms by using synthetic ballistic gelatin; these ultrasound phantoms can be used for simulation and are affordable, reproducible, and indefinitely shelf stable. The ultrasound images produced by these phantoms are comparable to those obtained from a real pediatric patient, including the sacral anatomy necessary for caudal epidural blocks, as validated by practicing pediatric anesthesiologists. Phantom models offer a more cost-effective alternative to commercially prepared phantoms, thereby expanding access to realistic simulations for neuraxial ultrasound in pediatric medical education, without the prohibitively high expense.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"11 ","pages":"e63682"},"PeriodicalIF":3.2,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12088609/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143989334","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}

{"title":"The Evolution of Medical Student Competencies and Attitudes in Digital Health between 2016-2022: A Comparative Cross-Sectional Study.","authors":"Paula Veikkolainen, Timo Tuovinen, Petri Kulmala, Erika Jarva, Jonna Juntunen, Anna-Maria Tuomikoski, Merja Männistö, Teemu Pihlajasalo, Jarmo Reponen","doi":"10.2196/67423","DOIUrl":"https://doi.org/10.2196/67423","url":null,"abstract":"<p><strong>Background: </strong>Modern healthcare systems worldwide are facing challenges, and digitalization is viewed as a way to strengthen healthcare globally. As healthcare systems become more digital, it's essential to assess healthcare professionals' competencies and skills to ensure they can adapt to new practices, policies, and workflows effectively.</p><p><strong>Objective: </strong>The aim of this study was to analyse how the attitudes, skills and knowledge of medical student concerning digital health have shifted from 2016 to 2022 in connection with the development of the national healthcare information system architecture utilising the Clinical Adoption Meta-Model framework.</p><p><strong>Methods: </strong>The study population consisted of fifth-year medical students from one University in Finland during 2016, 2021 and 2022. A survey questionnaire was administered comprising seven background questions and 16 statements rated on a five-point Likert scale assessing students' attitudes towards digital health and their self-perceived digital capabilities. The results were recategorized into a dichotomous scale. The statistical analysis employed Pearson's chi-square test. The Benjamini-Hochberg procedure was used for multiple variable correction.</p><p><strong>Results: </strong>The study included 215 medical students (n = 45 in 2016, n = 106 in 2021, and n = 64 in 2022) with an overall response rate of 53% (43% in 2016, 74% in 2021, and 42% in 2022). Throughout 2016, 2021, and 2022, medical students maintained positive attitudes towards using patient-generated information and digital applications in patient care. Their self-perceived knowledge of the national patient portal significantly improved, with agreement increasing by 35 percentage points from 2016 to 2021 (P<.001) and this trend continued in 2022 (P<.001). However, their perceived skills in using electronic medical records did not show significant changes. Additionally, students' perceptions of the impact of digitalization on health promotion improved markedly from 2016 to 2021 (with agreement rising from 53% to 78%, P=.002) but declined notably again by 2022.</p><p><strong>Conclusions: </strong>Medical students' attitudes and self-perceived competencies have shifted over the years, potentially influenced by the national health information system architecture developments. However, these positive changes have not followed a completely linear trajectory. To address these gaps, educational institutions and policymakers should integrate more digital health topics into medical curricula and provide practical experience with digital technologies to keep professionals up-to-date with the evolving healthcare environment.</p><p><strong>Clinicaltrial: </strong></p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144095062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global Health care Professionals' Perceptions of Large Language Model Use In Practice: Cross-Sectional Survey Study.","authors":"Ecem Ozkan, Aysun Tekin, Mahmut Can Ozkan, Daniel Cabrera, Alexander Niven, Yue Dong","doi":"10.2196/58801","DOIUrl":"10.2196/58801","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;ChatGPT is a large language model-based chatbot developed by OpenAI. ChatGPT has many potential applications to health care, including enhanced diagnostic accuracy and efficiency, improved treatment planning, and better patient outcomes. However, health care professionals' perceptions of ChatGPT and similar artificial intelligence tools are not well known. Understanding these attitudes is important to inform the best approaches to exploring their use in medicine.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;Our aim was to evaluate the health care professionals' awareness and perceptions regarding potential applications of ChatGPT in the medical field, including potential benefits and challenges of adoption.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We designed a 33-question online survey that was distributed among health care professionals via targeted emails and professional Twitter and LinkedIn accounts. The survey included a range of questions to define respondents' demographic characteristics, familiarity with ChatGPT, perceptions of this tool's usefulness and reliability, and opinions on its potential to improve patient care, research, and education efforts.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;One hundred and fifteen health care professionals from 21 countries responded to the survey, including physicians, nurses, researchers, and educators. Of these, 101 (87.8%) had heard of ChatGPT, mainly from peers, social media, and news, and 77 (76.2%) had used ChatGPT at least once. Participants found ChatGPT to be helpful for writing manuscripts (n=31, 45.6%), emails (n=25, 36.8%), and grants (n=12, 17.6%); accessing the latest research and evidence-based guidelines (n=21, 30.9%); providing suggestions on diagnosis or treatment (n=15, 22.1%); and improving patient communication (n=12, 17.6%). Respondents also felt that the ability of ChatGPT to access and summarize research articles (n=22, 46.8%), provide quick answers to clinical questions (n=15, 31.9%), and generate patient education materials (n=10, 21.3%) was helpful. However, there are concerns regarding the use of ChatGPT, for example, the accuracy of responses (n=14, 29.8%), limited applicability in specific practices (n=18, 38.3%), and legal and ethical considerations (n=6, 12.8%), mainly related to plagiarism or copyright violations. Participants stated that safety protocols such as data encryption (n=63, 62.4%) and access control (n=52, 51.5%) could assist in ensuring patient privacy and data security.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Our findings show that ChatGPT use is widespread among health care professionals in daily clinical, research, and educational activities. The majority of our participants found ChatGPT to be useful; however, there are concerns about patient privacy, data security, and its legal and ethical issues as well as the accuracy of its information. Further studies are required to understand the impact of ChatGPT and other large language models on clinical, ed","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"11 ","pages":"e58801"},"PeriodicalIF":3.2,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12088617/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144037600","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}