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. 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. 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. 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.

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. 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". 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. 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.

While large language models (LLMs) show promise in medical education, their comprehensive performance in specialized domains like medical laboratory science remains inadequately assessed. This study aimed to evaluate advanced LLMs on medical laboratory questions, assessing accuracy, natural language generation (NLG) quality, reasoning performance, and efficiency. 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. 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. 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.

Training health services administration (HSA) professionals is essential for the effective functioning of hospitals. Evidence suggests that training should emphasize social and decision-making competencies, as HSA professionals collaborate in an interprofessional environment. Simulation training (ST) for HSA in an interprofessional context is a promising approach. This study evaluated the effectiveness of such a training program on leadership self-efficacy, examined differences across professions and tested whether there was an interaction between profession and training effects. This retrospective study was conducted at the simulation center of Metropole Savoie Hospital in France. The 3-day program was mandatory for all HSA professionals, and included customized simulation scenarios, interprofessional collaboration and integration of aviation sector for their expertise in leadership skills employed in crisis management. Self-efficacy was measured by a specifically constructed questionnaire administered at baseline (T0), post training (T1) and 4 months post-training (T2). The ST program significantly increased leadership self-efficacy. Self-efficacy improved post-training (p. < 001, mean change = 31.67, 95% CI [28.06, 35.28], d = 1.75) and increased self-efficacy was maintained for 4 months (p < 0.001, η2p = 0.70, T0-T1 Δ = 32.05 [26.71-37.39], T0-T2 Δ = 2.43 [-7.77-2.90], η2p = 0.70). There was a significant main effect of profession with executive physicians reporting lower self-efficacy than other professionals combined (p < 0.001, mean difference = -22.56, 95% CI [14.83-30.30], η2p = 0.66). No significant interaction between profession and program was observed (p > 0.05). ST shows promise for enhancing leadership skills in HSA, particularly in an interprofessional context. Executive physicians remain a key group for targeted training, as their lower self-efficacy suggests, they may benefit from interventions aimed at strengthening leadership skills. Advancing in this direction will reinforce the findings.

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. 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. 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. 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. 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.

Postgraduate training in clinical laboratory diagnostics (professional degree) in China operates under a "four certificates in one" framework tightly coupled with standardized residency training. However, a structural recruitment-training mismatch has emerged: eligible applicants increasingly come from clinical medicine undergraduate programs with limited early exposure to laboratory medicine governance, quality systems, and post-analytical assurance, contributing to persistent under-enrollment and heterogeneous training experiences across schools. Using a strengths-weaknesses-opportunities-threats (SWOT) framework, this review synthesized objective enrollment signals and analyzed how policy constraints, competency gaps, and evolving service expectations jointly shape training feasibility. We also examined recent "laboratory physician training" pilot/experimental classes that front-load laboratory exposure and improve pathway continuity and translated these pilots into a rotation-wide competency framework with corresponding teaching activities and workplace-based assessment tools to support implementation across varied settings. Because these pilots are recent and outcome data remains limited, the implications are primarily policy- and design-based. Overall, this review contributes an actionable competency-alignment perspective for medical education and training reform, highlighting early exposure, structured reskilling/upskilling in quality management, interpretive reporting and clinical communication, and context-sensitive incorporation of digital/AI-enabled workflows where available.

This review evaluates the specific applications of Artificial Intelligence (AI) in prosthodontic and implant dentistry education, and discusses related educational challenges. 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. We included 213 articles focusing exclusively on AI applications in prosthodontic and implant dentistry education, covering theoretical learning, skill training, and clinical practice. 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. 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.