Advances in Medical Education and Practice最新文献

Background: Memes are a popular online communication tool that are participatory, playful and contextual in nature. While the use of meme creation as an education tool in higher education has been limited, meme creation requires students to reflect on material presented in the classroom, synthesize new content from learned concepts, and present it in a contextualized manner.

Objective: The objective of this study was to examine the level of reflection demonstrated in a meme creation assignment introduced into a master's level nutrition course which covered systemic and ethical issues related to nutrition, research, and clinical practice.

Methods: Participants were graduate-level dietetic students (n = 55) enrolled in an Evidence Based Practice course in a Master of Science/Dietetic Internship Program. Students were instructed to create a meme about content from the course. Memes were analyzed for depth of reflection using an existing quantitative framework and researchers conducted a hybrid thematic analysis.

Results: Of the 82 memes submitted, 9 (11%) were rated as reflection level 0 (description), 11 (13.4%) as level 1 (reflective description), 26 (31.7%) as level 2 (dialogic reflection), 15 (18.3%) as level 3 (transformative reflection) and 21 (25.6%) as level 4 (critical reflection). Four primary themes identified were Ethics, Philosophy of Science, Art of Science and Science and the Public.

Conclusion: Our findings confirm that the use of a meme creation assignment is an educational tool that promotes student reflection. Students displayed a higher-than-expected level of transformative or critical reflection which may be due to the playful, visual format of memes.

Background: While large language models (LLMs) show promise in medical education, their comprehensive performance in specialized domains like medical laboratory science remains inadequately assessed.

Purpose: This study aimed to evaluate advanced LLMs on medical laboratory questions, assessing accuracy, natural language generation (NLG) quality, reasoning performance, and efficiency.

Methods: We conducted a multi-faceted evaluation of three advanced LLMs (DeepSeek-R1, Gemini-2.5 Pro, GPT-5), benchmarking them against medical laboratory scientists and earlier ChatGPT versions. The evaluation utilized 493 questions sourced from the internal Medical Laboratory Test Bank of Wannan Medical College. These questions comprised both knowledge-based and reasoning-based single- and multiple-choice types (SCQs and MCQs). Performance was measured by accuracy, Macro-F1, response time, NLG scores (ROUGE-L, METEOR), and structured logical reasoning assessment. Appropriate statistical tests (including χ², Wilcoxon, ANOVA, and non-parametric alternatives) with post-hoc corrections were applied to determine significance.

Results: DeepSeek-R1's accuracy on total questions was 78.3%, nearing the 79.3% of the higher-performing senior expert. Notably, it excelled at complex reasoning-based MCQ, demonstrating an advantage over senior experts with an accuracy of 64.4%, compared to 58.7% (SMLS-1) and 56.7% (SMLS-2). While ChatGPT-5 was the fastest model, DeepSeek-R1 exhibited intermediate efficiency, aligning with human experts on SCQ but requiring more time for MCQ. In terms of NLG, DeepSeek-R1 consistently achieved the highest scores, with ROUGE-L scores of 0.36 ± 0.14 (Total Q), 0.33 ± 0.15 (SCQ), and 0.38 ± 0.13 (MCQ), and METEOR scores of 0.53 ± 0.19 (Total Q), 0.40 ± 0.17 (SCQ), and 0.63 ± 0.14 (MCQ). Furthermore, it significantly outperformed all other LLMs in logical reasoning comprehensiveness. A critical strength was its consistent integration of key negative findings, vital for diagnosis.

Conclusion: DeepSeek-R1 approaches or even surpasses senior expert performance in certain tasks, showing strong potential as an effective tool for education and assessment despite slower processing times.

Background: Cadaver dissection is a well-established method to teach neuroanatomy in medical school. However, the outcome on functional anatomical understanding and student experience has not been studied. The aim of this study was to compare traditional topographically based brain cadaver dissections with dissections based on functional white matter dissections with regard to functional-anatomical knowledge and student experience.

Methods: Pre-clinical medical students were randomly assigned to a control group with traditional two dimensional topographical cadaver dissections and to study groups with functionally based white matter dissections. The control dissections were performed as formerly planned by non-clinical anatomy tutors and the study dissections were planned and overseen by an experienced neurosurgeon. After the dissections, the students underwent a web-based questionnaire including four questions on topographical and functional neuroanatomy, and three questions on experience and self-evaluation of neuroanatomical knowledge.

Results: A total of 130 students were included, (n=33 in the control group and n=97 in the study group). Students in the study group scored higher on knowledge-based multiple-choice questions regarding speech and language, motor functions and the ventricular system; however, statistical significance was observed only for speech and language. They also scored higher in self-perceived knowledge after the dissections, although not statistically significant.

Discussion: Including functional and clinical aspects in brain cadaver improves anatomy teaching in pre-clinical medical students. The authors argue that it is of importance to integrate clinicians in the pre-clinical anatomy teaching.

As global healthcare systems evolve, medical training must advance not only in content but also in its moral responsibility to the learners, patients, and society. This perspective paper examines how well-intentioned training systems can inadvertently cause harm and argues for reframing medical education around applied expressions of the classical bioethics principles of safety, equity, and future readiness. Beyond traditional concerns such as consent and confidentiality, it highlights the structural dimensions of ethics: training equity, learner safety, and institutional accountability. Drawing on global and regional evidence, simulation-based medical education is presented not merely as a pedagogical innovation but as an ethical imperative that improves performance, reduces procedural errors, and enhances learner confidence. Drawing on these concerns, the paper introduces the SAFE Training Framework - Safety, Alignment, Faculty development, and Evaluation - as a practical model for embedding ethics into medical education. The conceptual framework emphasizes four essential anchors: ensuring Safety for learners and patients, Aligning training with ethical and contextual values, empowering Faculty through development and support, and Evaluating competence through authentic, future-oriented assessment. This framework, targeted at medical educators, training institutions, policymakers, and accrediting bodies responsible for shaping health-workforce quality and patient safety, can inform curriculum design and accreditation standards for medical education in Low- and Middle-Income countries. The discussion also addresses emerging challenges in the age of artificial intelligence, calling for balance between innovation and integrity in assessment. Strengthening health systems in West Africa and beyond will require more than moral intent; it demands practical, ethically grounded approaches to capacity building across every stage of medical training.

Purpose: Few studies have explored the challenges faced by medical students from widening participation (WP) backgrounds compared to their non-WP peers. This study aimed to identify these challenges in medical school and evaluate how well a national charity enabled them to address these challenges.

Participants and methods: A 26-item online Qualtrics questionnaire was circulated to all medical student volunteers within the charity Medics & Me. This explored potential challenges faced by students, including finances, networking opportunities, career development, and their sense of belonging in medical school. Fischer's exact test and unpaired t-testing were used to statistically compare mean scores between WP and non-WP students, and thematic analysis elicited student views on Medics & Me. Ethics approval was obtained for the study.

Results: Forty-seven out of a national cohort of 196 Medics & Me medical student volunteers (23.9%) responded to the questionnaire. 19 were from a WP background and 28 from non-WP backgrounds. WP students were significantly more likely than non-WP students to feel that finances impacted their studies (p=0.0152), more likely to work to fund their studies (p=0.0011), and less likely to consider their medical school as inclusive to the needs of all students. (p=0.0267). All students cited poor support with career development, networking, and pastoral care, and a feeling that they did not belong. Positive aspects of Medics & Me included the sense of community and networking opportunities. Key themes for improvement included clearer guidance for developing career portfolios, more networking opportunities, and closer working with medical schools.

Discussion: WP students reported significantly more concerns with finances and belonging than non-WP students. All students scored their medical school poorly for support, particularly regarding career development and networking opportunities. Medics & Me should consider supporting students with career development and networking opportunities and work closely with faculty.

Aim: Transitioning from preclinical to clinical training is a pivotal phase in medical education, accompanied by substantial psychosocial and academic challenges. There is limited research exploring the experiences of students in Northern Ireland (NI). This pilot study aims to examine the perspectives and lived experiences of third-year medical students at Queen's University Belfast (QUB) during their transition, and to identify ways in which institutional support can be strengthened to better address their needs.

Methods: This mixed-methods study employed a sequential design combining survey and focus groups. A survey was distributed to all third-year students, containing four Likert-scale and two open-text questions assessing preparedness, confidence, anxiety and support needs. Survey data were analysed descriptively, while focus group transcripts underwent thematic analysis using Braun and Clarke's framework.

Results: Thirty-nine students (13%) completed the survey. Participants described feeling unprepared and anxious. Two subsequent semi-structured focus groups (n=8 and n=5) explored these themes. Key themes included communication challenges, mental health concerns, mentorship and the hidden curriculum. Respondents expressed desire for enhanced wellbeing support from clinical tutors.

Conclusion: This exploratory study suggests that the transition to clinical training presents substantial challenges for some medical students at QUB. Given the low response rate and single-institution design, findings may not be generalizable. However, they indicate areas warranting further investigation, including communication strategies, structured mentorship programs and wellbeing support during transitions. Future research with larger, more representative samples is needed to validate these preliminary findings and inform evidence-based interventions.

Objective: This review evaluates the specific applications of Artificial Intelligence (AI) in prosthodontic and implant dentistry education, and discusses related educational challenges.

Data and sources: Searches in PubMed, Scopus, Wiley and Web of Science used targeted keywords including artificial intelligence, prosthodontics, education, implant dentistry, virtual simulation, and combinations of the keywords.

Study selection: We included 213 articles focusing exclusively on AI applications in prosthodontic and implant dentistry education, covering theoretical learning, skill training, and clinical practice.

Results: The review identified key AI applications: 1) Generative AI enhancing theoretical learning on Prosthodontics and Implant Dentistry for students and faculty; 2) AI-powered simulators and assessment tools improving laboratory and clinical skill training of Prosthodontics and Implant Dentistry. 3) Critical challenges regarding academic integrity, algorithmic bias, and data privacy were also highlighted.

Conclusions: AI offers transformative potential in medical education and translation by enhancing skill acquisition and assessment. Addressing the ethical and practical challenges is essential for its successful and responsible integration into the curriculum.

Objective: The global COVID-19 pandemic has had a profound impact on the education system. Education shifted to virtual methods, while there was not enough time to plan and choose a proper educational platform. In this study, we present an up-to-date review of the most commonly used virtual education platforms in Iran during the COVID-19 pandemic.

Methods: This narrative review systematically searched Persian and English articles (2020-2024) in Medline, EMBASE, Scopus, Web of Science, ERIC, SID, CIVILICA, and PubMed using keywords: "COVID-19", "virtual learning", "online learning", "distance learning", "post-COVID infection", "real and virtual simulation", and "educational platforms".

Results: Virtual classes have become increasingly popular during the pandemic. Adobe Connect, Sky Room, Skype, Big Blue Button, Google Meet, Gharar, Zoom, and Navaid System were the most commonly used platforms during the COVID pandemic in Iran. The most frequently utilized systems included Shad (predominant in general education and training) and Navid (leading in medical sciences). Shad had excelled in scalability and institutional integration but faced connectivity issues in rural areas. Despite its technical strengths, Navid was criticized because of insufficient interactivity and misalignment with learner needs in medical English.

Discussion: During COVID-19, online medical education in Iran relied mainly on domestic platforms, which have some limitations. To ensure future equity and competency, a shift toward hybrid models incorporating offline-capable Learning Management Systems (LMS), simulation, and digital literacy training is essential.

Artificial intelligence (AI) is increasingly used for documentation purposes in clinical practice, yet guidance for resident use is limited. Given the substantial documentation burden on medical trainees, AI-powered scribing tools may offer benefits, but their integration into residency training raises educational, supervisory, and patient safety considerations. This study aimed to assess the availability of resident-specific guidance on AI scribe use from major medical and specialty organizations and to summarize current evidence on AI scribes in residency. We reviewed five major medical and specialty society websites (AAD, AMA, ACGME, AAMC, ABMS) via website searches and direct emails and conducted a PubMed search for residency-focused studies. No organization provided resident-specific guidelines; the AMA noted the need for safe, accurate, and non-discriminatory AI use, and the AAMC issued general principles for the use of AI in medical education, none of which specifically address resident documentation. Literature review identified two studies evaluating AI-assisted documentation in the residency context. The studies suggest reduced documentation burden and cognitive load, with documentation quality comparable to resident physicians. These findings highlight the need for residency-specific AI scribe consensus guidelines that address resident review, note sign-off, accountability, patient consent, and scope of use to ensure safe and effective integration into training.

Purpose: Limited resources including staff, technology, and space in United Kingdom National Health Service (NHS) environments present challenges for working teaching and learning. Cumulative pressures impact mental health and prompt medical students to question career choices, leave the profession, or emigrate. There is little understanding of the experience of inclusivity in clinical environments for medical students. This pilot study explores students' perspectives of these environments, the barriers to formulating change, and possible solutions.

Materials and methods: Six diverse clinical medical students from the University of Manchester, based at a single NHS Trust, voluntarily participated in one of four recorded focus groups facilitated by a near-peer with protected characteristics. Low participation emphasised the importance of undertaking this pilot study to consider methods for future study design. Transcriptions were independently analysed by two reviewers to generate themes using NVivo.

Results: Key themes included positive experiences with resident doctors; negative interactions with patients; negative attitudes and poor role modelling from some staff; perceived lack of institutional support; and poor learning opportunities. Students suggested staff training and better support structures to improve learning environments.

Conclusion: This study highlights universal issues from the perspectives of minoritised students and emphasises their low participation in research. Authors suggest exploring clinical faculty support and training to improve learning environments, and innovations in study design for wider, more democratic and culturally sensitive participation.