JMIR Medical Education最新文献

{"title":"Assessing the Ability of a Large Language Model to Score Free-Text Medical Student Clinical Notes: Quantitative Study.","authors":"Harry B Burke, Albert Hoang, Joseph O Lopreiato, Heidi King, Paul Hemmer, Michael Montgomery, Viktoria Gagarin","doi":"10.2196/56342","DOIUrl":"10.2196/56342","url":null,"abstract":"<p><strong>Background: </strong>Teaching medical students the skills required to acquire, interpret, apply, and communicate clinical information is an integral part of medical education. A crucial aspect of this process involves providing students with feedback regarding the quality of their free-text clinical notes.</p><p><strong>Objective: </strong>The goal of this study was to assess the ability of ChatGPT 3.5, a large language model, to score medical students' free-text history and physical notes.</p><p><strong>Methods: </strong>This is a single-institution, retrospective study. Standardized patients learned a prespecified clinical case and, acting as the patient, interacted with medical students. Each student wrote a free-text history and physical note of their interaction. The students' notes were scored independently by the standardized patients and ChatGPT using a prespecified scoring rubric that consisted of 85 case elements. The measure of accuracy was percent correct.</p><p><strong>Results: </strong>The study population consisted of 168 first-year medical students. There was a total of 14,280 scores. The ChatGPT incorrect scoring rate was 1.0%, and the standardized patient incorrect scoring rate was 7.2%. The ChatGPT error rate was 86%, lower than the standardized patient error rate. The ChatGPT mean incorrect scoring rate of 12 (SD 11) was significantly lower than the standardized patient mean incorrect scoring rate of 85 (SD 74; P=.002).</p><p><strong>Conclusions: </strong>ChatGPT demonstrated a significantly lower error rate compared to standardized patients. This is the first study to assess the ability of a generative pretrained transformer (GPT) program to score medical students' standardized patient-based free-text clinical notes. It is expected that, in the near future, large language models will provide real-time feedback to practicing physicians regarding their free-text notes. GPT artificial intelligence programs represent an important advance in medical education and medical practice.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"10 ","pages":"e56342"},"PeriodicalIF":3.2,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11327632/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141907879","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":"Roles and Responsibilities of the Global Specialist Digital Health Workforce: Analysis of Global Census Data.","authors":"Kerryn Butler-Henderson, Kathleen Gray, Salma Arabi","doi":"10.2196/54137","DOIUrl":"10.2196/54137","url":null,"abstract":"<p><strong>Background: </strong>The Global Specialist Digital Health Workforce Census is the largest workforce survey of the specialist roles that support the development, use, management, and governance of health data, health information, health knowledge, and health technology.</p><p><strong>Objective: </strong>This paper aims to present an analysis of the roles and functions reported by respondents in the 2023 census.</p><p><strong>Methods: </strong>The 2023 census was deployed using Qualtrics and was open from July 1 to August 13, 2023. A broad definition was provided to guide respondents about who is in the specialist digital health workforce. Anyone who self-identifies as being part of this workforce could undertake the survey. The data was analyzed using descriptive statistical analysis and thematic analysis of the functions respondents reported in their roles.</p><p><strong>Results: </strong>A total of 1103 respondents completed the census, with data reported about their demographic information and their roles. The majority of respondents lived in Australia (n=870, 78.9%) or New Zealand (n=130, 11.8%), with most (n=620, 56.3%) aged 35-54 years and identifying as female (n=720, 65.3%). The top four occupational specialties were health informatics (n=179, 20.2%), health information management (n=175, 19.8%), health information technology (n=128, 14.4%), and health librarianship (n=104, 11.7%). Nearly all (n=797, 90%) participants identified as a manager or professional. Less than half (430/1019, 42.2%) had a formal qualification in a specialist digital health area, and only one-quarter (244/938, 26%) held a credential in a digital health area. While two-thirds (502/763, 65.7%) reported undertaking professional development in the last year, most were self-directed activities, such as seeking information or consuming online content. Work undertaken by specialist digital health workers could be classified as either leadership, functional, occupational, or technological.</p><p><strong>Conclusions: </strong>Future specialist digital health workforce capability frameworks should include the aspects of leadership, function, occupation, and technology. This largely unqualified workforce is undertaking little formal professional development to upskill them to continue to support the safe delivery and management of health and care through the use of digital data and technology.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"10 ","pages":"e54137"},"PeriodicalIF":3.2,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11327619/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141907880","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":"Using the Kirkpatrick Model to Evaluate the Effect of a Primary Trauma Care Course on Health Care Workers' Knowledge, Attitude, and Practice in Two Vietnamese Local Hospitals: Prospective Intervention Study.","authors":"Ba Tuan Nguyen, Van Anh Nguyen, Christopher Leigh Blizzard, Andrew Palmer, Huu Tu Nguyen, Thang Cong Quyet, Viet Tran, Marcus Skinner, Haydn Perndt, Mark R Nelson","doi":"10.2196/47127","DOIUrl":"10.2196/47127","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The Primary Trauma Care (PTC) course was originally developed to instruct health care workers in the management of patients with severe injuries in low- and middle-income countries (LMICs) with limited medical resources. PTC has now been taught for more than 25 years. Many studies have demonstrated that the 2-day PTC workshop is useful and informative to frontline health staff and has helped improve knowledge and confidence in trauma management; however, there is little evidence of the effect of the course on changes in clinical practice. The Kirkpatrick model (KM) and the knowledge, attitude, and practice (KAP) model are effective methods to evaluate this question.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The aim of this study was to investigate how the 2-day PTC course impacts the satisfaction, knowledge, and skills of health care workers in 2 Vietnamese hospitals using a conceptual framework incorporating the KAP model and the 4-level KM as evaluation tools.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;The PTC course was delivered over 2 days in the emergency departments (EDs) of Thanh Hoa and Ninh Binh hospitals in February and March 2022, respectively. This study followed a prospective pre- and postintervention design. We used validated instruments to assess the participants' satisfaction, knowledge, and skills before, immediately after, and 6 months after course delivery. The Fisher exact test and the Wilcoxon matched-pairs signed rank test were used to compare the percentages and mean scores at the pretest, posttest, and 6-month postcourse follow-up time points among course participants.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 80 health care staff members attended the 2-day PTC course and nearly 100% of the participants were satisfied with the course. At level 2 of the KM (knowledge), the scores on multiple-choice questions and the confidence matrix improved significantly from 60% to 77% and from 59% to 71%, respectively (P&lt;.001), and these improvements were seen in both subgroups (nurses and doctors). The focus of level 3 was on practice, demonstrating a significant incremental change, with scenarios checklist points increasing from a mean of 5.9 (SD 1.9) to 9.0 (SD 0.9) and bedside clinical checklist points increasing from a mean of 5 (SD 1.5) to 8.3 (SD 0.8) (both P&lt;.001). At the 6-month follow-up, the scores for multiple-choice questions, the confidence matrix, and scenarios checklist all remained unchanged, except for the multiple-choice question score in the nurse subgroup (P=.005).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The PTC course undertaken in 2 local hospitals in Vietnam was successful in demonstrating improvements at 3 levels of the KM for ED health care staff. The improvements in the confidence matrix and scenarios checklist were maintained for at least 6 months after the course. PTC courses should be effective in providing and sustaining improvement in knowledge and trauma care practice in other LMICs such as Vi","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"10 ","pages":"e47127"},"PeriodicalIF":3.2,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11284612/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141749212","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":"Appraisal of ChatGPT's Aptitude for Medical Education: Comparative Analysis With Third-Year Medical Students in a Pulmonology Examination.","authors":"Hela Cherif, Chirine Moussa, Abdel Mouhaymen Missaoui, Issam Salouage, Salma Mokaddem, Besma Dhahri","doi":"10.2196/52818","DOIUrl":"10.2196/52818","url":null,"abstract":"<p><strong>Background: </strong>The rapid evolution of ChatGPT has generated substantial interest and led to extensive discussions in both public and academic domains, particularly in the context of medical education.</p><p><strong>Objective: </strong>This study aimed to evaluate ChatGPT's performance in a pulmonology examination through a comparative analysis with that of third-year medical students.</p><p><strong>Methods: </strong>In this cross-sectional study, we conducted a comparative analysis with 2 distinct groups. The first group comprised 244 third-year medical students who had previously taken our institution's 2020 pulmonology examination, which was conducted in French. The second group involved ChatGPT-3.5 in 2 separate sets of conversations: without contextualization (V1) and with contextualization (V2). In both V1 and V2, ChatGPT received the same set of questions administered to the students.</p><p><strong>Results: </strong>V1 demonstrated exceptional proficiency in radiology, microbiology, and thoracic surgery, surpassing the majority of medical students in these domains. However, it faced challenges in pathology, pharmacology, and clinical pneumology. In contrast, V2 consistently delivered more accurate responses across various question categories, regardless of the specialization. ChatGPT exhibited suboptimal performance in multiple choice questions compared to medical students. V2 excelled in responding to structured open-ended questions. Both ChatGPT conversations, particularly V2, outperformed students in addressing questions of low and intermediate difficulty. Interestingly, students showcased enhanced proficiency when confronted with highly challenging questions. V1 fell short of passing the examination. Conversely, V2 successfully achieved examination success, outperforming 139 (62.1%) medical students.</p><p><strong>Conclusions: </strong>While ChatGPT has access to a comprehensive web-based data set, its performance closely mirrors that of an average medical student. Outcomes are influenced by question format, item complexity, and contextual nuances. The model faces challenges in medical contexts requiring information synthesis, advanced analytical aptitude, and clinical judgment, as well as in non-English language assessments and when confronted with data outside mainstream internet sources.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"10 ","pages":"e52818"},"PeriodicalIF":3.2,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11303904/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141753019","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":"Can an Online Course, <i>Life101: Mental and Physical Self-Care</i>, Improve the Well-Being of College Students?","authors":"Mahtab Jafari","doi":"10.2196/50111","DOIUrl":"10.2196/50111","url":null,"abstract":"<p><strong>Unlabelled: </strong>The COVID-19 pandemic has had a significant impact on the mental health of college students worldwide. As colleges shifted to online instruction, students faced disruptions and increased stressors, leading to a decline in mental health that appears to continue in the postpandemic era. To alleviate this problem, academic institutions have implemented various interventions to address mental health issues; however, many of these interventions focus on a single approach and lack diverse delivery methods. This viewpoint introduces the concept of a multimodal self-care online course, Life101: Mental and Physical Self-Care, and discusses the potential effectiveness of such an intervention in improving students' well-being. The course combines evidence-based interventions and incorporates interactive lectures, workshops, and guest speakers. Pre- and postcourse surveys were conducted over a span of 4 academic terms to evaluate the impact of this course on the well-being and self-care practices of students. The survey data suggest positive outcomes in students taking Life101, including the adoption of healthier habits, reduced stress levels, and increased knowledge and practice of self-care techniques. Life101 represents a novel multimodality intervention to address the epidemic of mental health issues faced by students today. By implementing similar evidence-based multimodal didactic curricula across campuses, academic institutions may be able to better equip students to navigate challenges and promote their overall well-being.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"10 ","pages":"e50111"},"PeriodicalIF":3.2,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11284613/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141749210","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":"Data-Driven Fundraising: Strategic Plan for Medical Education.","authors":"Alireza Jalali, Jacline Nyman, Ouida Loeffelholz, Chantelle Courtney","doi":"10.2196/53624","DOIUrl":"10.2196/53624","url":null,"abstract":"<p><strong>Unlabelled: </strong>Higher education institutions, including medical schools, increasingly rely on fundraising to bridge funding gaps and support their missions. This paper presents a viewpoint on data-driven strategies in fundraising, outlining a 4-step approach for effective planning while considering ethical implications. It outlines a 4-step approach to creating an effective, end-to-end, data-driven fundraising plan, emphasizing the crucial stages of data collection, data analysis, goal establishment, and targeted strategy formulation. By leveraging internal and external data, schools can create tailored outreach initiatives that resonate with potential donors. However, the fundraising process must be grounded in ethical considerations. Ethical challenges, particularly in fundraising with grateful medical patients, necessitate transparent and honest practices prioritizing donors' and beneficiaries' rights and safeguarding public trust. This paper presents a viewpoint on the critical role of data-driven strategies in fundraising for medical education. It emphasizes integrating comprehensive data analysis with ethical considerations to enhance fundraising efforts in medical schools. By integrating data analytics with fundraising best practices and ensuring ethical practice, medical institutions can ensure financial support and foster enduring, trust-based relationships with their donor communities.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"10 ","pages":"e53624"},"PeriodicalIF":3.2,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11284734/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141749211","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 Utility of Wearable Cameras in Developing Examination Questions and Answers on Physical Examinations: Preliminary Study.","authors":"Sho Fukui, Taro Shimizu, Yuji Nishizaki, Kiyoshi Shikino, Yu Yamamoto, Hiroyuki Kobayashi, Yasuharu Tokuda","doi":"10.2196/53193","DOIUrl":"10.2196/53193","url":null,"abstract":"<p><strong>Unlabelled: </strong>To assess the utility of wearable cameras in medical examinations, we created a physician-view video-based examination question and explanation, and the survey results indicated that these cameras can enhance the evaluation and educational capabilities of medical examinations.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"10 ","pages":"e53193"},"PeriodicalIF":3.2,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11273174/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141735239","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":"Development and Implementation of a Safety Incident Report System for Health Care Discipline Students During Clinical Internships: Observational Study.","authors":"Eva Gil-Hernández, Irene Carrillo, Mercedes Guilabert, Elena Bohomol, Piedad C Serpa, Vanessa Ribeiro Neves, Maria Maluenda Martínez, Jimmy Martin-Delgado, Clara Pérez-Esteve, César Fernández, José Joaquín Mira","doi":"10.2196/56879","DOIUrl":"10.2196/56879","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Patient safety is a fundamental aspect of health care practice across global health systems. Safe practices, which include incident reporting systems, have proven valuable in preventing the recurrence of safety incidents. However, the accessibility of this tool for health care discipline students is not consistent, limiting their acquisition of competencies. In addition, there is no tools to familiarize students with analyzing safety incidents. Gamification has emerged as an effective strategy in health care education.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to develop an incident reporting system tailored to the specific needs of health care discipline students, named Safety Incident Report System for Students. Secondary objectives included studying the performance of different groups of students in the use of the platform and training them on the correct procedures for reporting.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This was an observational study carried out in 3 phases. Phase 1 consisted of the development of the web-based platform and the incident registration form. For this purpose, systems already developed and in use in Spain were taken as a basis. During phase 2, a total of 223 students in medicine and nursing with clinical internships from universities in Argentina, Brazil, Colombia, Ecuador, and Spain received an introductory seminar and were given access to the platform. Phase 3 ran in parallel and involved evaluation and feedback of the reports received as well as the opportunity to submit the students' opinion on the process. Descriptive statistics were obtained to gain information about the incidents, and mean comparisons by groups were performed to analyze the scores obtained.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The final form was divided into 9 sections and consisted of 48 questions that allowed for introducing data about the incident, its causes, and proposals for an improvement plan. The platform included a personal dashboard displaying submitted reports, average scores, progression, and score rankings. A total of 105 students participated, submitting 147 reports. Incidents were mainly reported in the hospital setting, with complications of care (87/346, 25.1%) and effects of medication or medical products (82/346, 23.7%) being predominant. The most repeated causes were related confusion, oversight, or distractions (49/147, 33.3%) and absence of process verification (44/147, 29.9%). Statistically significant differences were observed between the mean final scores received by country (P&lt;.001) and sex (P=.006) but not by studies (P=.47). Overall, participants rated the experience of using the Safety Incident Report System for Students positively.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study presents an initial adaptation of reporting systems to suit the needs of students, introducing a guided and inspiring framework that has garnered positive acceptance among students. Through this endeavor, a p","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"10 ","pages":"e56879"},"PeriodicalIF":3.2,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11294782/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141634820","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":"Curriculum Frameworks and Educational Programs in AI for Medical Students, Residents, and Practicing Physicians: Scoping Review.","authors":"Raymond Tolentino, Ashkan Baradaran, Genevieve Gore, Pierre Pluye, Samira Abbasgholizadeh-Rahimi","doi":"10.2196/54793","DOIUrl":"10.2196/54793","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The successful integration of artificial intelligence (AI) into clinical practice is contingent upon physicians' comprehension of AI principles and its applications. Therefore, it is essential for medical education curricula to incorporate AI topics and concepts, providing future physicians with the foundational knowledge and skills needed. However, there is a knowledge gap in the current understanding and availability of structured AI curriculum frameworks tailored for medical education, which serve as vital guides for instructing and facilitating the learning process.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The overall aim of this study is to synthesize knowledge from the literature on curriculum frameworks and current educational programs that focus on the teaching and learning of AI for medical students, residents, and practicing physicians.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We followed a validated framework and the Joanna Briggs Institute methodological guidance for scoping reviews. An information specialist performed a comprehensive search from 2000 to May 2023 in the following bibliographic databases: MEDLINE (Ovid), Embase (Ovid), CENTRAL (Cochrane Library), CINAHL (EBSCOhost), and Scopus as well as the gray literature. Papers were limited to English and French languages. This review included papers that describe curriculum frameworks for teaching and learning AI in medicine, irrespective of country. All types of papers and study designs were included, except conference abstracts and protocols. Two reviewers independently screened the titles and abstracts, read the full texts, and extracted data using a validated data extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. We adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist for reporting the results.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Of the 5104 papers screened, 21 papers relevant to our eligibility criteria were identified. In total, 90% (19/21) of the papers altogether described 30 current or previously offered educational programs, and 10% (2/21) of the papers described elements of a curriculum framework. One framework describes a general approach to integrating AI curricula throughout the medical learning continuum and another describes a core curriculum for AI in ophthalmology. No papers described a theory, pedagogy, or framework that guided the educational programs.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This review synthesizes recent advancements in AI curriculum frameworks and educational programs within the domain of medical education. To build on this foundation, future researchers are encouraged to engage in a multidisciplinary approach to curriculum redesign. In addition, it is encouraged to initiate dialogues on the integration of AI into medical curriculum planning and to investigate the developm","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"10 ","pages":"e54793"},"PeriodicalIF":3.2,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11294785/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141634819","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 Ability of ChatGPT in Paraphrasing Texts and Reducing Plagiarism: A Descriptive Analysis.","authors":"Soheil Hassanipour, Sandeep Nayak, Ali Bozorgi, Mohammad-Hossein Keivanlou, Tirth Dave, Abdulhadi Alotaibi, Farahnaz Joukar, Parinaz Mellatdoust, Arash Bakhshi, Dona Kuriyakose, Lakshmi D Polisetty, Mallika Chimpiri, Ehsan Amini-Salehi","doi":"10.2196/53308","DOIUrl":"10.2196/53308","url":null,"abstract":"<p><strong>Background: </strong>The introduction of ChatGPT by OpenAI has garnered significant attention. Among its capabilities, paraphrasing stands out.</p><p><strong>Objective: </strong>This study aims to investigate the satisfactory levels of plagiarism in the paraphrased text produced by this chatbot.</p><p><strong>Methods: </strong>Three texts of varying lengths were presented to ChatGPT. ChatGPT was then instructed to paraphrase the provided texts using five different prompts. In the subsequent stage of the study, the texts were divided into separate paragraphs, and ChatGPT was requested to paraphrase each paragraph individually. Lastly, in the third stage, ChatGPT was asked to paraphrase the texts it had previously generated.</p><p><strong>Results: </strong>The average plagiarism rate in the texts generated by ChatGPT was 45% (SD 10%). ChatGPT exhibited a substantial reduction in plagiarism for the provided texts (mean difference -0.51, 95% CI -0.54 to -0.48; P<.001). Furthermore, when comparing the second attempt with the initial attempt, a significant decrease in the plagiarism rate was observed (mean difference -0.06, 95% CI -0.08 to -0.03; P<.001). The number of paragraphs in the texts demonstrated a noteworthy association with the percentage of plagiarism, with texts consisting of a single paragraph exhibiting the lowest plagiarism rate (P<.001).</p><p><strong>Conclusions: </strong>Although ChatGPT demonstrates a notable reduction of plagiarism within texts, the existing levels of plagiarism remain relatively high. This underscores a crucial caution for researchers when incorporating this chatbot into their work.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"10 ","pages":"e53308"},"PeriodicalIF":3.2,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11250043/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141581004","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}