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The competence of ChatGPT (Chat Generative Pre-Trained Transformer) in non-English languages is not well studied. This study compared the performances of GPT-3.5 (Generative Pre-trained Transformer) and GPT-4 on the Japanese Medical Licensing Examination (JMLE) to evaluate the reliability of these models for clinical reasoning and medical knowledge in non-English languages. This study used the default mode of ChatGPT, which is based on GPT-3.5; the GPT-4 model of ChatGPT Plus; and the 117th JMLE in 2023. A total of 254 questions were included in the final analysis, which were categorized into 3 types, namely general, clinical, and clinical sentence questions. The results indicated that GPT-4 outperformed GPT-3.5 in terms of accuracy, particularly for general, clinical, and clinical sentence questions. GPT-4 also performed better on difficult questions and specific disease questions. Furthermore, GPT-4 achieved the passing criteria for the JMLE, indicating its reliability for clinical reasoning and medical knowledge in non-English languages. GPT-4 could become a valuable tool for medical education and clinical support in non-English-speaking regions, such as Japan.

BackgroundFew tools or rubrics exist to assess the quality of integrated STEM curricula, and existing tools focus on checklists of characteristics of integrated STEM. While such instruments provide important information about the presence and quality of certain curricular components, they do not assess the level and nature of integration of the curriculum as a whole. Thus, this study explores the development of a process focused to understand the nature of integration within a STEM curriculum unit.FindingsA conceptual flow graphic (CFG) was constructed for 50 integrated STEM curriculum units. Patterns in the nature of the interdisciplinary connections were used to categorize and understand the nature of integration and curricular coherence within each unit. The units formed four broad types of integrated STEM curricula: (i) coherent science unit with loosely connected engineering design challenge (EDC), (ii) engineering design-focused unit with limited connections to science content, (iii) engineering design unit with science content as context, and (iv) integrated and coherent STEM units. All physical science units were in the integrated and coherent category with strong conceptual integration between the main science concepts and the EDC. Curricula based in the Earth and life sciences generally lacked conceptual integration between the science content and the EDC and relied on the engineering design process to provide a coherent storyline for the unit.ConclusionsOur study shows that engineering practices can serve as a contextual integrator within a STEM unit. The utilization of an EDC also provides the potential for conceptual integration because engineering is grounded in the application of science and mathematics. Integrated STEM curricula that purposefully include science and mathematics concepts necessary to develop solutions to the EDC engage students in authentic engineering experiences and provide conceptual integration between the disciplines. However, the alignment of grade-level science standards with the EDC can be problematic, particularly in life science and Earth science. The CFG process provides a tool for determining the nature of integration between science and mathematics content and an EDC. These connections can be conceptual and/or contextual, as both forms of integration are appropriate depending on the instructional goals.

The COVID-19 pandemic has brought virtual web-based learning to the forefront of medical education as training programs adapt to physical distancing challenges while maintaining the rigorous standards of medical training. Social media has unique and partially untapped potential to supplement formal medical education. The aim of this review is to provide a summary of the incentives, applications, challenges, and pitfalls of social media-based medical education for both trainees and educators. We performed a literature review via PubMed of medical research involving social media platforms, including Facebook, Twitter, Instagram, YouTube, WhatsApp, and podcasts. Papers were reviewed for inclusion based on the integrity and power of the study. The unique characteristics of social media platforms such as Facebook, Twitter, Instagram, YouTube, WhatsApp, and podcasts endow them with unique communication capabilities that serve different educational purposes in both formal and informal education settings. However, contemporary medical education curricula lack widespread guidance on meaningful use, application, and deployment of social media in medical education. Clinicians and institutions must evolve to embrace the use of social media platforms for medical education. Health care professionals can approach social media engagement in the same ethical manner that they would with patients in person; however, health care institutions ultimately must enable their health care professionals to achieve this by enacting realistic social media policies. Institutions should appoint clinicians with strong social media experience to leadership roles to spearhead these generational and cultural changes. Further studies are needed to better understand how health care professionals can most effectively use social media platforms as educational tools. Ultimately, social media is here to stay, influencing lay public knowledge and trainee knowledge. Clinicians and institutions must embrace this complementary modality of trainee education and champion social media as a novel distribution platform that can also help propagate truth in a time of misinformation, such as the COVID-19 pandemic.

In inclusive education, students with different needs learn in the same context. With the advancement of artificial intelligence (AI) technologies, it is expected that they will contribute further to an inclusive learning environment that meets the individual needs of diverse learners. However, in Japan, we did not find any studies exploring current needs in an actual special needs context. In this study, we used the learning and evidence analysis framework (LEAF) as a learning analytics-enhanced learning environment and employed Active Reading as an example learning task to investigate the challenges and possibilities of applying AI to inclusive education in the future. Two students who attended a resource room formed the context. We investigated learning logs in the LEAF system while each student executed a given learning task. We detected specific learning behaviors from the logs and explored the challenges and future potential of learning with AI technology, considering human involvement in orchestrating inclusive educational practices.

BackgroundCountries around the world have struggled to implement education policies and practices to encourage more female youths to pursue Science, Technology, Engineering, and Mathematics (STEM). This has resulted in a persistent and sizeable gender gap in science and mathematics subjects in some countries. Using mixed-methods sequential explanatory design, this paper explores an educational intervention—specifically, a 3-day design thinking workshop—in Japan, designed to change female youths’ perceptions regarding STEM topics. Framed using a constructivist approach to learning, the workshops aimed to engender creative confidence, empathy, and global competence among youths.ResultsThe findings show that female youths who participated in the workshop had increased interest in engineering, greater creative confidence, more positive perceptions of STEM, higher levels of empathy and pro-social factors, and a more varied outlook on career options. We argue that this short intervention had a strong influence on the female youths’ mindsets, self-images, and perceptions of STEM.ConclusionThis study provides empirical support that a short intervention can produce positive change in how female youths relate to STEM. In gendered societies, an innovative method like design thinking has the potential to revitalize education curriculum in ways that spur female youths’ confidence and creativity, enabling them to imagine a career in the field of STEM.

Background:\\nIn Japan, some residents develop mental health problems. In previous studies, it was reported that\\nlong working hours might be a cause of stress reaction such as depression. There were some reports that\\ncompared residents with 80 or more working hours with those with less than 80 working hours. However, many\\nresidents are practically detained for extra-long time, designated as 100 h or more per week, for medical practice,\\ntraining, self-study, etc. There have been few reports on extra-long hours of work. This study evaluated the working\\nenvironment and the amount of stress experienced by first-year residents, and examined the relationship between\\nlong working hours and depression, especially in the group of extra-long working hours.\\n\\nMethods:\\nThe study included 1241 first-year residents employed at 250 training hospitals in 2011. A self-report questionnaire was administered at the beginning of the residency and 3 months later to collect data on\\ndemographics, depressive symptoms, and training conditions (e.g., duration of work, sleep, disposable time, and\\nnight shift). Depressive symptoms were rated using the Center for Epidemiologic Studies Depression Scale.\\n\\nResults:\\nThe mean duration of work per week was 79.4 h, with 97 residents (7.8%) working 100 h or more. At\\n3 months, clinically significant depressive symptoms were reported by 45.5% of residents working 100 or more h\\nper week, which proportion was significantly greater than that for respondents working less than 60 h (P< 0.001).\\nMultivariate logistic regression analysis showed that a working week of 80 to 99.9 h was associated with a 2.83 fold\\nhigher risk and 100 h or more was associated with a 6.96-fold higher risk of developing depressive symptoms\\ncompared with a working week of less than 60 h.\\n\\nConclusion:\\nWorking excessively long hours was significantly associated with development of depressive\\nsymptoms. Proper management of resident physicians\\n'working hours is critical to maintaining their physical and\\nmental health and to improve the quality of care they provide.

Background It is known that empathic communication is important for physicians to achieve higher patient satisfaction and health outcomes. Emotional intelligence (EI), empathy and personality in medical students predict students’ individual disposition and their emotional and empathic perceptions. This study aimed to investigate: 1) The association between empathy, EI and personality, and 2) Gender differences in the association between empathy, EI and personality. Method Participants were 357 1st year medical students from 2008 to 2011 at one medical school in Japan. Students completed self-report questionnaires comprising three validated instruments measuring EI: Trait Emotional Intelligence Questionnaire-Short Form (TEIQue-SF), empathy: Jefferson Scale of Physician Empathy- student version (JSPE) and personality: NEO-Five-Factor Inventory (NEO-FFI), which explores 5 dimensions of personality Neuroticism (N), Extraversion (E), Openness to experience (O), Agreeableness (A), and Conscientiousness (C). Results Pearson Correlations showed weak association between TEIQue-SF and JSPE. TEIQue-SF and NEO-FFI showed positive correlation for E and C, and strong negative correlation for N and weak positive correlation for A and O. Weak positive correlation between JSPE and the NEO-FFI were observed for E and A. Although effect sizes were small, N, A and empathy were significantly higher in females (unpaired t-test). However, hierarchical multiple-regression analysis when controlling for gender and personality showed no association between EI, empathy and gender. A, TEIQue-SF and N were found to make small contributions in respect of predictions for JSPE. Personality contributed significantly to the prediction of TEIQue-SF. N had the largest independent negative contribution (β = − 0,38). Conclusion In our study population of 1st year medical students, females had significantly higher N, A and empathy scores than males. Medical students’ N score was strongly negatively associated with EI. Empathy was weakly associated with EI and A. However, when controlling gender and personality in regression analysis, gender did not affect EI and empathy, rather personality is the most important factor. Our findings indicate that N is a major factor that negatively affects EI. It is important to mitigate N using thoughtful training, taking into account students’ personalities, to reduce N. In future studies, we will assess how communication trainings for students might enhance EI.

Background Social determinants of health (SDH) are intricately intertwined with various social and economic factors. Reflection is essential for learning about SDH. However, only a few reports have focused on reflection in SDH programs; most were cross-sectional studies. We aimed to longitudinally evaluate a SDH program in a community-based medical education (CBME) curriculum that we introduced in 2018 based on the level of reflection and content on SDH in students’ reports. Methods Study design: General inductive approach for qualitative data analysis. Education program: A 4-week mandatory clinical clerkship in general medicine and primary care at the University of Tsukuba School of Medicine in Japan was provided to all fifth- and sixth-year medical students. Students underwent a 3-week rotation in community clinics and hospitals in suburban and rural areas of Ibaraki Prefecture. After a lecture on SDH on the first day, students were instructed to prepare a structural case description based on encounters during the curriculum. On the final day, students shared their experiences in a small group session and submitted a report on SDH. The program was continuously improved and faculty development was provided. Study participants: Students who completed the program during October 2018–June 2021. Analysis: Levels of reflection were categorized as reflective, analytical, or descriptive. The content was analyzed based on the Solid Facts framework. Results We analyzed 118 reports from 2018–19, 101 reports from 2019–20, and 142 reports from 2020–21. There were 2 (1.7%), 6 (5.9%), and 7 (4.8%) reflective reports; 9 (7.6%), 24 (23.8%), and 52 (35.9%) analytical reports; and 36 (30.5%), 48 (47.5%), and 79 (54.5%) descriptive reports, respectively. The others were not evaluable. The number of Solid Facts framework items in reports were 2.0 ± 1.2, 2.6 ± 1.3, and 3.3 ± 1.4, respectively. Conclusions Students’ understanding of SDH deepened as the SDH program in the CBME curriculum improved. Faculty development might have contributed to the results. Reflective understanding of SDH might require more faculty development and integrated education of social science and medicine.

Whether GPT-4, the conversational artificial intelligence, can accurately diagnose and triage health conditions and whether it presents racial and ethnic biases in its decisions remain unclear. We aim to assess the accuracy of GPT-4 in the diagnosis and triage of health conditions and whether its performance varies by patient race and ethnicity. We compared the performance of GPT-4 and physicians, using 45 typical clinical vignettes, each with a correct diagnosis and triage level, in February and March 2023. For each of the 45 clinical vignettes, GPT-4 and 3 board-certified physicians provided the most likely primary diagnosis and triage level (emergency, nonemergency, or self-care). Independent reviewers evaluated the diagnoses as \"correct\" or \"incorrect.\" Physician diagnosis was defined as the consensus of the 3 physicians. We evaluated whether the performance of GPT-4 varies by patient race and ethnicity, by adding the information on patient race and ethnicity to the clinical vignettes. The accuracy of diagnosis was comparable between GPT-4 and physicians (the percentage of correct diagnosis was 97.8% (44/45; 95% CI 88.2%-99.9%) for GPT-4 and 91.1% (41/45; 95% CI 78.8%-97.5%) for physicians; P=.38). GPT-4 provided appropriate reasoning for 97.8% (44/45) of the vignettes. The appropriateness of triage was comparable between GPT-4 and physicians (GPT-4: 30/45, 66.7%; 95% CI 51.0%-80.0%; physicians: 30/45, 66.7%; 95% CI 51.0%-80.0%; P=.99). The performance of GPT-4 in diagnosing health conditions did not vary among different races and ethnicities (Black, White, Asian, and Hispanic), with an accuracy of 100% (95% CI 78.2%-100%). P values, compared to the GPT-4 output without incorporating race and ethnicity information, were all .99. The accuracy of triage was not significantly different even if patients' race and ethnicity information was added. The accuracy of triage was 62.2% (95% CI 46.5%-76.2%; P=.50) for Black patients; 66.7% (95% CI 51.0%-80.0%; P=.99) for White patients; 66.7% (95% CI 51.0%-80.0%; P=.99) for Asian patients, and 62.2% (95% CI 46.5%-76.2%; P=.69) for Hispanic patients. P values were calculated by comparing the outputs with and without conditioning on race and ethnicity. GPT-4's ability to diagnose and triage typical clinical vignettes was comparable to that of board-certified physicians. The performance of GPT-4 did not vary by patient race and ethnicity. These findings should be informative for health systems looking to introduce conversational artificial intelligence to improve the efficiency of patient diagnosis and triage.