Introduction: The COVID-19 pandemic compelled educational institutions worldwide to transition abruptly from traditional face-to-face instruction to remote learning environments. This sudden shift posed particular challenges for medical education, where both theoretical instruction and hands-on clinical practice are essential. Objective: This study aims to evaluate the readiness levels and attitudes of medical faculty members and students regarding educational technologies implemented during the pandemic. Methods: A cross-sectional survey was conducted at Gazi University Faculty of Medicine involving 635 students and 107 academic staff. Data were collected using validated instruments: the Online Learning Readiness Scale for students and an E-learning Readiness and Attitude Scale for faculty members. Quantitative analyses were performed using descriptive statistics, t-tests, and ANOVA. Results: The results revealed that while students demonstrated moderate levels of readiness, especially in self-directed learning and online communication, motivation and technological self-efficacy were comparatively lower. Faculty members exhibited high competence in ICT use but showed lower scores in confidence and attitudes toward e-learning. Nearly half of the students and approximately 30% of the faculty believed that online education failed to meet its intended educational objectives. Conclusion: These findings suggest that digital infrastructure alone is insufficient to ensure the effectiveness of distance education in medical training. Motivation, pedagogical support, and structured faculty development programs are necessary to improve engagement and learning outcomes. The study highlights the need for blended learning models that integrate synchronous and asynchronous elements with face-to-face clinical components. Aligning technology-enhanced learning strategies with medical curriculum demands will be essential in improving the resilience and quality of medical education in future crises.

Video-based assignments are used in medical education, yet expert scoring is time-intensive. Large language models (LLMs) offer scalable alternatives, but their validity for evaluating multimodal student work is uncertain. We examined whether a state-of-the-art LLM (Gemini 2.5 Pro) could approximate expert scoring of medical student video presentations in evidence-based medicine. A total of 139 student submissions were evaluated by an experienced faculty member (reference standard) and by the LLM under two prompting strategies: (i) rubric-only and (ii) critical expert-style. Scores across 12 rubric items (maximum 95 points) were compared using paired t-tests, effect sizes, and Bland-Altman analysis. Expert scoring yielded a mean total of 62.0 (SD 13.6). The rubric-only prompt systematically overestimated performance (mean 87.7, SD 8.0, P < 0.001; bias -25.7). The critical prompt produced lower scores (mean 53.5, SD 10.4, P < 0.001; bias +8.5). At the item level, rubric-only prompting aligned better with mechanical tasks (e.g., keywords and referencing), whereas the critical prompt penalized appraisal and synthesis disproportionately. Prompting strategy substantially influenced LLM scoring, generating opposite biases relative to expert evaluation. The novel contribution of this study is that prompt strategy can alter not only the magnitude but also the direction of scoring bias. Calibration approaches, such as context engineering, may help align AI scoring with expert judgment. While AI-generated feedback shows promise for formative assessment, reliable summative use requires careful validation.

Aims: Many valuable names have been noted on the history pages about the development of modern medical education in the world for over 100 years. From this perspective, as a prominent name of the history of modern medical education in Türkiye, Prof. Dr. İskender Sayek is evaluated in this study.Methods: The study was designed as qualitative, in which data sources were both semi-structured interview with Prof. Dr. İskender Sayek and the documents obtained from Google, Google Scholar, PubMed, Youtube, and Spotify. All data were evaluated based on the studies on the leadership styles defined previously as well as the authors’ professional communication with Prof. Sayek for more than 15 years in the field of medical education.Findings: The documents were evaluated regarding the development of modern medical education within the framework of official duties, academic activities and civil society activities in medical education; the interview was evaluated under the themes of openness to communication and information sharing, impressiveness, time management and level of expertise-satisfaction. From his evaluation reports and other publications on medical education to his integration implications of modern medical education methods and his foundation of TEPDAD (‘Tıp Eğitimi Programları Değerlendirme ve Akreditasyon Derneği’ in Turkish: ‘Association for Evaluation and Accreditation of Medical Education Programs’) and the Department of Medical Education and Informatics at Hacettepe University Faculty of Medicine during his deanship, all the findings suggest that Prof.Sayek has been a leader in the development of modern medical education in Türkiye, especially in the last 25 years. His activities in medical education include a wide spectrum, ranging from structural and instructional changes in formal medical education to civil activities aimed at raising awareness about medical education such as condition of medical education, social reliability and social accountability among not only people working in the field of health and health education but also ones outside of health, all of which demonstrate that he has characteristics of many leadership styles such as visionary leadership, instructional leadership and academic leadership. Based on his formal and informal activities that have led to not only structural improvements in medical education in Türkiye but also changes in minds ensuring the development to continue based on science, we conclude that transformative leadership is his dominant leadership style for Prof. Sayek. In addition to the leadership part of this study on Prof. Sayek, we also present our interview with him. It includes various topics ranging from his observation on medical education in the last 60 years to his future recommendations and expectations, especially on the goals for modern medical education in the Republic of Türkiye’s second century, providing additional value to the study.Conclusions: Prof. Sayek's professional life, both formal and informal, is an inspiring example of transformational leadership in medical education, as it demonstrates how medical education can be improved both intellectually and practically, and how its sustainability can be ensured based on science. His current activities and future recommendations in our interview confirm this conclusion as well.

BACKGROUND: This study investigates the relationships between medical students’ learning styles and their study approaches. While learning styles have been criticized and debated, study approaches are recognized for their association with academic success. Understanding these dynamics can enhance educational practices in medical education by catering to diverse student needs. METHODS: The study employs a descriptive research model with 1724 medical students using the VARK questionnaire and ASSIST inventory. We applied cluster analysis with the k-means algorithm to group students based on their learning styles. The Chi-square test of independence was used to explore the association between learning styles and study approaches. RESULTS: Kinesthetic and Aural styles were most common, followed by Read-Write. Surface-apathetic was the most frequent study approach, followed by strategic and deep approaches. Multimodal learners showed a strong positive association with the deep approach and a negative one with the strategic approach. Kinesthetic learners also favored the deep approach, while Read-Write learners aligned with the strategic approach. CONCLUSIONS: The findings underscore the importance of recognizing the interaction between medical students’ learning style preferences and their adopted study approaches. These results can guide medical educators in designing more diverse and student-centered pedagogical strategies. Overall, the study highlights the value of promoting learners’ metacognitive awareness and fostering the development of flexible learning strategies rather than rigidly aligning instruction with specific learning styles.

Background: Multiple-choice questions (MCQs) are widely used in medical education due to their objectivity, efficiency, and ability to cover a broad knowledge base. Case-based MCQs provide additional benefits by evaluating students’ clinical reasoning and decision-making skills. In psychiatry education, unique challenges arise from overlapping symptoms, reliance on subjective reports, and the absence of objective diagnostic tools. The aim of this study was to administer MCQs generated through template-based automatic item generation (AIG) in psychiatry to medical students and to evaluate their psychometric properties (difficulty and discrimination indices). Methods: Following ethical approval from XXX University Ethics Committee, the study included 138 volunteer students (61.6%) from a total of 224 who completed psychiatry clerkship during the 2023–2024 and 2024–2025 academic years. From a pool of 1189 template-based automatically generated questions, 22 were randomly selected to form the exam. The test was administered face-to-face under supervision, and students were not informed of the origin of the items. Difficulty indices were calculated as the proportion of correct answers, while discrimination indices were computed by comparing the performance of the top 27% and bottom 27% groups. Results: The mean exam score was 15.21 ± 3.55 out of 22. The average difficulty index was 0.69, classifying the exam as “easy.” Of the items, 63.6% were very easy, 9.1% easy, and 27.3% moderate. The most difficult item concerned somatization (0.33), whereas the easiest was related to bipolar disorder (0.92). Discrimination indices ranged from 0.19 to 0.70, with an average of 0.37. Ten items (45.6%) demonstrated excellent discrimination, eleven (50%) acceptable, and one (4.5%) poor. The highest discrimination was observed in the schizophreniform disorder item (0.70), while the lowest was in the postpartum psychosis item (0.19). Conclusions: This study represents the first direct implementation of template-based AIG in Turkish psychiatry education. The findings demonstrated that automatically generated MCQs achieved acceptable psychometric standards in terms of both difficulty and discrimination. Template-based AIG may reduce faculty workload while ensuring consistent and high-quality question development. However, further refinement is needed to generate items assessing higher-order cognitive processes. Multicenter comparative studies could provide stronger evidence for the integration of AIG into medical education assessments.

Amaç: Yapay zekâ, diş hekimliği alanında klinik uygulamalarda hızla kendine yer bulmuş ve çeşitli alanlarda önemli katkılar sağlamıştır. Klinik kullanımının getirdiği faydaların yanı sıra diş hekimliği eğitimi bağlamında otomatik öğrenme sistemleriyle desteklenen ve yapay zeka tabanlı yazılımlar, halen gelişime açık ve potansiyel vaat eden bir alandır. Diş hekimliği eğitimine yapay zekanın entegrasyonu eğitici ve öğrenciler açısından faydalar sağlayan güncel ve inovatif bir yaklaşımdır. Diş hekimliği fakültelerinde geleneksel eğitim modellerini dönüştürme potansiyeline sahip olan yapay zekâ tabanlı yaklaşımlar, öğrenme kalitesini artırmak ve öğrenci başarısını desteklemek amacıyla zeki öğretim sistemlerini devreye sokmaktadır. Bu durum, diş hekimliği eğitiminde öğrenme süreçleri, öğretme, ölçme-değerlendirme ve geri bildirim süreçlerinin gözden geçirilmesine ve hatta köklü değişimlere kapı aralayabilecek bir potansiyele sahiptir. Yöntem: Bu derleme, geleneksel bir yöntemle hazırlanmış olup, diş hekimliği eğitiminde yapay zekâ uygulamalarının mevcut durumu ve potansiyel etkilerini incelemektedir. Son zamanlarda yapay zekanın hızla gelişmesiyle birlikte literatürde tıp eğitimi alanında da yaygın kullanımına ilişkin yayınlar artmaktadır. Mezuniyet öncesi eğitim öğretimde, müfredat içeriğinde, ölçme değerlendirmede, üç boyutlu sanal eğitim ortamları yaratılmasında ve diş hekimliği eğitiminin gelecek perspektifleri açısından yapay zekanın getirdiği yenilikler vurgulanmıştır. Yapay zekanın diş hekimliği eğitimindeki yeri eğiticiler, öğrenciler ve eğitim sistemleri açısından literatür örnekleriyle paylaşılmıştır. Bulgular: Tıp eğitiminde yapay zekâ kullanımı, sağlık alanında etkin teorik ve pratik eğitim açısından sürekli bir dönüşüm geçirerek kapsamını genişletmektedir. Yapay zekâ destekli uygulama ve yazılımlar ile sanal gerçeklik simülatörlerinden haptik cihazlara, robotik hastalara kadar pek çok inovatif yenilik, diş hekimliği eğitiminin zorlu klinik öncesi ve klinik eğitim süreçlerine hızla entegre olmaktadır. Bu teknolojiler, öğrencilerin beklenen motor beceri seviyesine daha kısa sürede ulaşmalarını sağlamakta ve klinik öncesi dönemde gerçek hasta deneyimine benzer çalışmalar yapmalarına olanak tanımaktadır. Klinik dönemde ise yapay zekâ tabanlı sistemler klinik hataları azaltarak güvenli dental uygulamalar yapılmasına, hasta bulgularının analizinde, tedavi planlamasında karar vermede yardımcı olmakta böylece tedavi kalitesini artırmaktadır. Bu teknolojilerin eğitim, müfredat geliştirilmesi, ölçme değerlendirilmesi gibi süreçlerde kullanımı, hem eğiticiler hem de öğrenciler açısından diş hekimliği eğitiminin ilerlemesine önemli katkılar sunmaktadır. Öğrencilerin ve eğiticilerin bu teknolojileri kabul edilebilir bulması, eğitim süreçlerinde yapay zekânın etkinliğini artıran başka bir önemli faktördür. Sonuç: Diş hekimliği öğrencilerinin ve eğiticilerin yapay zekâ destekli uygulamalar konusunda etkin birer kullanıcı olmaları, hem meslektaşlarının hem de hastalarının eğitimi konusunda önemli bir rol oynamalarını gerektirmektedir. Özellikle yapay zekâ tabanlı teknolojilerin kullanıldığı durumlarda, öğrencilerin, hasta yönetiminde yüz yüze deneyim kazanmaları oldukça önemli bir faktördür. Yapay zeka tabanlı uygulamaların kullanıldığı durumlar, yapay zekanın diş hekimliği eğitimindeki yeri, avantaj ve dezavantajları, kısıtlılıkları tartışılmıştır. Yapay zekanın diş hekimliği eğitiminde aktif kullanılması, öğrenci merkezli öğrenmeye yönelik olarak yenilikçi bir yaklaşım sağlamaktadır. Eğitime yapay zekanın entegrasyonu hem diş hekimliğinde mezuniyet öncesi eğitiminde hem de yaşam boyu öğrenmede gelecekte klinik uygulamalarda inovatif teknolojilerin etkin bir şekilde kullanılmasını sağlayacaktır.

Amaç: Tıp eğitiminde mezuniyet öncesi akademik başarının mezuniyet sonrası akademik başarıya etkisi halen cevaplanmamış ve merak uyandıran bir konudur. Mezuniyet öncesi bilgi düzeyi ile mezuniyet sonrası akademik başarı arasında zayıf bir ilişki saptanırken klinik performans değerlendirmeye alındığında ise daha anlamlı ilişki düzeyi saptanmaktadır. Ancak mevcut çalışmaların heterojen olması, farklı değerlendirme kriterlerinin kullanılması nedeniyle bu konuda kesin bir yargıya varılması mümkün olmamaktadır. Bu çalışmada tıp fakültesi öğrencilerinin mezuniyet öncesi akademik başarılarının, mezuniyet sonrası akademik başarılarını yordama geçerliliğinin araştırılması amaçlandı. Yöntem: Kesitsel tipteki araştırmaya Gazi Üniversitesi Tıp Fakültesi’ni herhangi bir tarihte bitirmiş ve 2021 yılında Gazi Üniversitesi Tıp Fakültesi’nin herhangi bir bölümünde mezuniyet sonrası tıp eğitimi alan 124 katılımcı dahil edildi. Katılımcıların mezuniyet öncesi akademik performansları mezuniyet ortalama puanı ve Tıpta Uzmanlık Sınavı (TUS) puanı ile; mezuniyet sonrası akademik başarıları ise beceri sınavı puanı ve Mesleki Yeterlilik Formu (MYF) kullanılarak değerlendirildi. MYF, 8 bölümden ve 40 sorudan oluşmakta ve medikal bilgi, hasta bakımı, profesyonellik, insanlarla iletişim yeteneği, uygulamaya dayalı öğrenme ve geliştirme, görev bilinci, araştırma yeteneği, kişisel özellikler alt bölümlerini içermekte olup Cronbach alfa katsayısı 0.9740 olarak hesaplandı. Kategorik değişkenler sayı ve yüzde ile; sürekli değişkenler aritmetik ortalama, standart sapma ile gösterildi. İstatistiksel analizde frekans dağılımları, t-testi, ANOVA, ki-kare testi ve Spearman korelasyon analizi kullanıldı. İstatistiksel anlamlılık düzeyi p

OBJECTIVE:While AI-generated feedback has shown promise in medical education, prior studies have only used AI for feedback, with question design handled by human experts, and the process required human involvement. This study aimed to evaluate the effectiveness of a fully automated AI-based system that generates both multiple-choice questions (MCQs) and personalized feedback, without any human input, on improving diagnostic reasoning in preclinical medical students. DESIGN:A prospective, parallel-group, interventional study. The intervention group (Year-1 students) received AI-generated MCQs and feedback over 5 days using a web platform, coded via "vibe coding," with spaced repetition. The diagnoses covered included 5 abdominal pain conditions: acute appendicitis, acute cholecystitis, acute pancreatitis, acute gastroenteritis, and nephrolithiasis. Diagnostic performance was assessed via an Objective Structured Video Examination (OSVE), immediately and 2 weeks postintervention. The control group (Year-2 students) completed the OSVE once. SETTING:Gazi University Faculty of Medicine, Ankara, Turkiye; institutional academic setting focused on undergraduate medical education. PARTICIPANTS:Thirty-eight Year-1 medical students completed the intervention. Thirty-three Year-2 students served as a non-randomized control group. All intervention participants completed the immediate assessment; 30 completed the delayed assessment. RESULTS:Intervention participants outperformed the control group in diagnosing the 5 abdominal pain conditions immediately after the intervention (p < 0.001) and at the 2-week follow-up (p < 0.001). Postintervention expert review confirmed the accuracy of all AI-generated questions and identified minimal issues in 0.6% of feedback statements. Total AI cost was $0.51. CONCLUSIONS:A fully automated, low-cost AI system without human in the loop during content generation can significantly enhance illness scripts in preclinical medical students. Early engagement with such tools may help students strengthen surgical diagnostic skills and derive greater benefit from clinical environments. This kind of tools may transform how clinical reasoning is taught in resource-limited or high-volume educational settings.

OBJECTIVE:This study aims to evaluate the suitability of the automatic item generation (AIG) for producing Turkish case-based multiplechoice questions (MCQs) in psychiatry. METHOD:The study was planned as a descriptive study. In the first stage, topics were determined and a cognitive model was created by subject matter experts. In the second stage, a question template was created, variables were determined, the format of answer options was organized, and two equivalent templates of question content with different combinations were created. In the final stage, questions were generated using Python-based software based on these models. Following the question generation, random samples were selected and evaluated by experienced educators using a structured form. RESULTS:A total of 1189 questions were generated, with 11 questions sampled for each diagnosis. In the evaluation conducted by experts, six of the questions were deemed appropriate for each parameter, while minor corrections were suggested for five questions. It was stated that all the questions assess clinical reasoning skills rather than factual recall. CONCLUSION:The template-based AIG method allows for the rapid and effective production of high-quality questions needed in medical education. The study demonstrated that AIG in the Turkish language for generating MCQs that assess clinical reasoning is applicable in the field of psychiatry. This method enables the production of a large number of questions in a short time, enriched with various combinations. Keyword: Automated item generation, clinical reasoning, medical education, multiple choice question, psychiatry education.

Objective: Globalization and increased workforce mobility have led to a rise in international medical professionals. While healthcare needs vary across societies, core competencies for specialist doctors within their respective fields may exhibit similarities. This systematic review examined global variations in obstetrics and gynecology (OBGYN) curricula to inform strategies for enhancing training and improving patient outcomes. Methods: We searched the PubMed, Scopus, Web of Science, and Education Resources Information Center databases up to July 3, 2024, using the keywords postgraduate education, postgraduate medical education, residency and gynecology and obstetrics, gynecology, or obstetrics. Results: Out of 3850 studies, four articles were selected based on selection criteria. While the length and structure of training in obstetrics and gynecology varies from country to country, the common goal is to train physicians who can perform their work safely and independently. Reduced working hours in current residency programs may hinder trainees' ability to master the expanded range of clinical skills required of modern physicians. Alternative training settings like workshops, short courses, and eLearning modules are being implemented to address this. While rotations are offered in most programs, their implementation was not documented. A common weakness across curricula is the lack of detailed information regarding assessment methods. While rotations are offered in most programs, their implementation remains undocumented. Factors such as the health system, the scope and reach of screening programs, religion, and technical development play a significant role in shaping curriculum requirements. OBGYN training should be adapted to the specific needs of each country.

OBJECTIVE:To evaluate the performance of large language models (ChatGPT-4o and Claude 3.5 Sonnet) to generate script concordance test (SCT) items for assessing clinical reasoning in obstetrics and gynecology. METHODS:This cross-sectional study involved the generation of SCT items for five common diagnostic topics in obstetrics and gynecology in primary care settings. A total of 16 panelists evaluated the AI-generated SCT items against 11 predefined criteria. Descriptive statistics were used to compare the models' performance across criteria. RESULTS:ChatGPT-4o had an overall agreement rate of 90.57% for SCT items meeting the quality criteria, while Claude 3.5 Sonnet achieved 91.48%. The criterion with the lowest scores was "The scenario is of appropriate difficulty for medical students," with ChatGPT-4o rated at 71.25% and Claude 3.5 Sonnet at 76.25%. CONCLUSION:Large language models can generate SCT items that effectively assess clinical reasoning; however, further refinement is required to ensure the appropriate level of difficulty for medical students. These findings highlight the potential of AI to enhance the efficiency of SCT generation in obstetrics and gynecology within primary care settings.

BACKGROUND:Warnings are commonly used to signal the fallibility of AI systems like ChatGPT in clinical decision-making. Yet, little is known about whether such disclaimers influence medical students' diagnostic behaviour. Drawing on the Judge-Advisor System (JAS) theory, we investigated whether the warning alters advice-taking behaviour by modifying perceived advisor credibility. METHOD:In this randomized controlled trial, 186 fourth-year medical students evaluated three clinical vignettes with two diagnostic options. Each case was specifically designed to include the presentations of both diagnoses to make the case ambiguous. Students were randomly assigned to receive feedback either with (warning arm) or without (no-warning arm) a prominently displayed warning ('ChatGPT can make mistakes. Check important info'.). After submitting their initial response, students received ChatGPT-attributed disagreeing diagnostic feedback explaining why the alternate diagnosis was correct. Then they were given the opportunity to revise their original choice. Advice-taking was measured by whether students changed their diagnosis after viewing AI input. We analysed change rates, weight-of-advice (WoA) and used mixed-effects models to assess intervention effects. RESULTS:The warning did not influence diagnostic changes (15.3% no-warning vs. 15.9% warning; OR = 1.09, 95% CI: 0.46-2.59, p = 0.84). The WoA was 0.15 (SD = 0.36), significantly lower than the 0.30 average in prior JAS meta-analysis (p < 0.001). Among students who retained their original diagnosis, the warning group showed a tendency toward providing explanations on why they disagree with the AI advisor (60% vs. 51%, p = 0.059). CONCLUSIONS:The students underweight AI's diagnostic advice. The disclaimer did not alter students' use of AI advice, suggesting that their perceived credibility of ChatGPT was already near a behavioural floor. This finding supports the existence of a credibility threshold, beyond which additional cautionary cues have limited effect. Our results refine advice-taking theory and signal that simple warnings may be insufficient to ensure calibrated trust in AI-supported learning.

Developing high-quality multiple-choice questions (MCQs) for medical school exams is effortful and time-consuming. In this study, we investigated the ability of ChatGPT to generate case-based anatomy MCQs with acceptable levels of item difficulty and discrimination for medical school exams. We used ChatGPT to generate case-based anatomy MCQs for an endocrine and urogenital system exam based on a framework for artificial intelligence (AI)-assisted item generation. The questions were evaluated by experts, approved by the department, and administered to 502 second-year medical students (372 Turkish-language, 130 English-language). The items were analyzed to determine the discrimination and difficulty indices. The item discrimination indices ranged from 0.29 to 0.54, indicating acceptable differentiation between high- and low-performing students. All items in Turkish (six out of six) and five out of six in English met the higher discrimination threshold (≥ 0.30) required for large-scale standardized tests. The item difficulty indices ranged from 0.41 to 0.89, most items falling within the moderate difficulty range (0.20-0.80). Therefore, it was concluded that ChatGPT can generate case-based anatomy MCQs with acceptable psychometric properties, offering a promising tool for medical educators. However, human expertise remains crucial for reviewing and refining AI-generated assessment items. Future research should explore AI-generated MCQs across various anatomy topics and investigate different AI models for question generation.

Objective: This study compared medical students and experts, and evaluated a frames-to-video AI-generated problem-based learning (PBL) trigger against its scene-matched human-made animated counterpart in terms of evaluations and preferences. Study Design: A mixed-methods study was conducted at a medical school. Two scene-matched videos were used: an AI-generated video and an animated (human-made) video. Students (n=210; Years 2-5) viewed both videos in counterbalanced order and rated eight 5-point Likert items for each; they also indicated their preferred video for engagement, emotional impact, and PBL use. A multidisciplinary expert panel (n=104) evaluated only the AI video on comparable items and provided open-ended comments. Mann-Whitney-U tests compared experts with students on the AI video; Wilcoxon signed-rank tests compared students' ratings across videos. Qualitative data underwent thematic analysis. Results: Students rated the AI-generated video significantly higher than the animated video on all eight items (all p <=.026) and preferred it for engagement (83.8%), emotional impact (81.0%), and PBL use (79.0%). Experts' ratings of the AI video were also high and exceeded students' ratings on visual quality, distraction avoidance, and visual consistency (p <=.001). Qualitative themes highlighted realism, suitability for PBL sessions, and strong engagement, while suggested improvements included micro-continuity, pronunciation, and body language. Conclusion: Within the PBL context, a frames-to-video AI workflow produced a fully synthetic trigger that was preferred by students and endorsed by experts. AI-generated triggers appear feasible, acceptable, and educationally promising, provided attention is given to fine-grained audiovisual continuity and communication cues.

Tıp öğrencilerinin gelecekleri için kariyer planlaması, uzmanlık seçimlerini ve mesleki kariyer seçeneklerini etkilemektedir. Bu çalışmada tıp fakültesi dördüncü sınıf öğrencilerinin kariyer planlama konusundaki farkındalık durumlarını ve bu bağlamda eğitim ihtiyaçlarını değerlendirmektir. Metod : 333 adet dördüncü sınıf tıp öğrencisi ile kesitsel bir çalışma olarak planlanmıştır. Katılımcılardan ayrıntılı bir literatür taraması ve uzman görüşleri alınarak hazırlanan bir anketi cevaplamaları istenmiştir. Anket içeriği açısından demografik özellikleri, tıp fakültesini seçme nedenlerini, kariyer ve uzmanlık alan tercihlerini, uzmanlık alan seçimini etkileyen faktörleri, kariyer planlama ile ilgili destek alma durumu ve desteğe duyulan ihtiyacı kapsamaktadır. Ayrıca öğrencilere kariyer yapılandırma envanteri uygulanmıştır. Bulgular: Katılımcıların kadın / erkek oranı 1.22’di (183/150). Öğrencilerin %94,2'si (312/331) bir uzmanlık alanı seçmeyi planlamıştır. Seçimler çalışma koşulları, kişisel ilgi, mali kaygılar ve gerekli uzmanlık sınavı puanı gibi faktörlerden etkilenmiştir. Öğrencilerin %75,3'ü kariyer planlama desteğine ihtiyaç duyduklarını ifade ederken, %85,8'i yeterli rehberlik almadıklarını düşünmektedir. Kız öğrenciler, erkek öğrencilere kıyasla kariyer danışmanlığına daha fazla ihtiyaç duymaktadır. Ayrıca, özellikle kız öğrenciler arasında cerrahi uzmanlık alanlarına olan ilginin az olduğu gözlemlenmiş ve bu da gelecekte bu alanlarda olası potansiyel bir boşluğa işaret etmiştir. Kariyer yapılandırma anketinin hazırlık aşamasındaki düşük ortalamalar (2.59–2.87), konu hakkında desteğe ihtiyaç duyabilecekleri açısından değerlendirilmesi gerekliliğini göstermektedir. Sonuç: Çalışmamızın sonucunda tıp eğitiminde kariyer yapılandırma eğitimi ve hizmetleri ile mentorluk programlarının gerekliliğini gözlenmiştir. Öğrencilere bu yaklaşımlar ile doğru ve bilinçli karar vermeleri desteklenebilir. Sağlık eğitiminde dengeli ve süreklilik sağlayacak politika planlamalarında bu durumunda göz önünde bulundurulması önemli olduğu değerlendirilmektedir.

AIM This study aimed to develop a blended training module focusing on ethical approaches within general surgery residency training and to assess the impact of this training on participants. METHODS Based on the literature review and input from both general surgery residents and trainers, 14 topics were identified, and corresponding learning objectives were formulated. The training was conducted through a blended learning module, which encompassed online video presentations alongside face-to-face sessions involving real-life cases. Assessment of the training involved administering test-formatted exams both before and after the training, which assessed the learning objectives of the 14 topics. These exams comprised multiple-choice questions and true/false inquiries based on case-based propositions. Additionally, feedback regarding the training was solicited from the residents. RESULTS The study involved 20 general surgery residents. Assessment revealed a statistically significant increase in exam success among the residents after the training (p<0.001). Additionally, feedback indicated that the training model was effective. CONCLUSIONS Developing a blended learning module that combines online and face-to-face education, supplemented with real-life case studies, and incorporating discussions on ethical dilemmas during face-to-face sessions, along with assessment through exams, will significantly enhance the proficiency of residents in surgical ethics.

Objective: Artificial Intelligence (AI) offers opportunities for radiologists to enhance workflow efficiency, perform faster and repeatable segmentation, and detect lesions more easily. The aim of this study is to investigate the current knowledge and general attitudes of radiology resident physicians towards AI. Additionally, it seeks to assess the current state of AI/ML/DL education in radiology residency, the awareness and use of available educational resources. Methods: A cross-sectional study was conducted using an online survey from October 2023 to February 2024. The survey included demographic data, AI knowledge, attitudes towards AI, and the role of AI in medical education. Survey questions were developed based on literature and reviewed by experts in medical education and radiology. Results: The study included 155 participants (38.7% female) with an average age of 28.81 +/- 4.77 years. About 80.6% were aware of AI terms, with a mean knowledge score of 3.02 +/- 1.39 on a 7-point Likert scale. Most participants (90.3%) had no programming knowledge. Only 22.6% used AI tools occasionally. The majority (73.4%) believed AI would change radiology's future, though only 10.3% felt radiologists' jobs were at risk. Regarding AI education, 84.5% reported no formal training, and awareness of online resources was low. Conclusion: The study found that while awareness of AI among radiology residents is high, their knowledge and practical use of AI tools are limited. AI education is largely absent from residency programs, and awareness of online educational resources is low. These findings highlight the need for integrating AI training into radiology education and increasing awareness of available resources.

Aim: The clerkship period in medical schools is an integrated learning experience that is also helpful in choosing a future career. The aim of this study was to evaluate the effect of both general surgery clerkshipship and awareness of learning styles on specialty preferences of medical students. Methods: In this study, which was planned as an educational intervention, a questionnaire questioning specialty preferences and a learning style scale were administered to fourth-year medical students before their general surgery clerkships. Following the questionnaire, brief training was given about specialty branches and learning styles. After the clerkship, the students were asked to answer the questionnaire again about their specialty preferences. Changes between clerkship training, learning style awareness and specialty preferences were evaluated. Results: One hundred eight students participated in the study (M:81, F:27). The mean age was 23.0. The most important factor in choosing a specialty is professional satisfaction (64%) with the work in that specialty. The approach of the trainers in the clerkship training (67%) was to focus on the most influential mind-changing factors for the choice of specialty. The most common learning styles among female students were kinesthetic and visual, while the most common learning style among male students was auditory. However, the learning style of students who preferred surgical branches was generally in the kinesthetic group. Conclusions: An effective clerkship program in which students encounter effective positive role models, gain knowledge about their field of specialization and learning styles, and increase their awareness will provide the right guidance in their specialization preferences.

Objectives: This study aims to determine the learning styles of pediatric surgery residents and develop applicable recommendations for their education based on the obtained data. Patients and methods: The study was conducted as an online survey using Kolb's Learning Style Inventory (LSI) to classify the learning styles of pediatric surgery residents between January 2021 and December 2021. The survey was distributed nationwide to 97 pediatric surgery residents in Türkiye. The form included demographic data (age, sex, year in residency) and Kolb's LSI questions. The LSI consists of 12 questions. The participants were first divided into groups according to their learning styles and sex. Then, to determine the relationship between clinical experience and learning styles, the participants were divided into three groups according to their duration of residency (≤1 year [Group 1], 2-3 years [Group 2], 4-5 years [Group 3]). Results: The survey form was sent to 97 residents, with 61 (63%) of them completing the form (25 males, 36 females, mean age: 28.8±2.6 years; range, 24 to 38 years). The most common learning styles among the residents were assimilator and diverger, both equally represented (27.9%). A significant difference was found between sex and learning styles (p=0.049), with the divergent learning style more common among male residents (44%) and the assimilator learning style more prevalent among female residents (39%). According to clinical experience, no significant difference was found among the groups (p=0.227). The assimilator learning style was more prevalent in senior residents, while the divergent learning style was more common in first-year residents. No significant difference was observed between age and learning styles. Conclusion: The frequent use of divergent and assimilator learning styles among pediatric surgery residents indicates a strong emphasis on theoretical learning and meticulous application of knowledge in practice. Based on our study results, we suggest that pediatric surgery instructors should incorporate more theoretical education into the curriculum to better prepare residents for potential surgical challenges.

Amaç: Tele-sağlık ve teletıp SARS CoV-2019 (COVID19) pandemisi sonrasında adını daha sık duyduğumuz sağlık hizmeti sunumu uygulamalarıdır. Dünya Sağlık Örgütü tele-sağlığı “Bireylerin ve toplulukların sağlığını geliştirmek amacıyla mesafenin kritik bir faktör olduğu durumlarda bilgi ve iletişim teknolojilerini kullanarak hastalık ve yaralanmaların teşhisi, tedavisi ve önlenmesi, araştırma, değerlendirme anları ve sağlık hizmeti sağlayıcılarının sürekli eğitiminde geçerli bilgi alış-verişi için sağlık çalışanları tarafından sağlık hizmetlerinin sunulması” olarak tanımlamaktadır. Teletıp ise “bir hastanın klinik sağlık durumunu iyileştirmek için elektronik iletişim yoluyla bir taraftan diğerine tıbbi bilgilerin aktarılmasıdır” Teletıp aslında tele-sağlığın bir alt başlığı olmasına rağmen günümüzde bu iki sözcük anlamdaş olarak kullanılmakta ve daha çok kabul görmektedir. Bu yazıda amaç konuyla ilgili tanımları okuyucularla paylaşmak; ayrıca mezuniyet öncesi ve sonrası tıp eğitimi programlarında teletıp eğitiminin müfredatla bütünleştirilmesi ile ilgili örnek deneyimleri sunmaktır. Gereç ve Yöntem: Geleneksel derleme yöntemiyle hazırlanan bu yazıda öncelikle teletıp ve ilişkili kavramların tanımları anlatılmıştır. Dünyadaki ve ülkemizdeki tarihsel sürecin paylaşılmasının ardından avantajları ve kısıtlılıkları tartışılmıştır. COVID-19 süreciyle ilişkisine değinilmiş ve bu durumun uzaktan sağlık hizmetleri sunumu konusunda tıp öğrencilerinin de bilgi ve deneyim sahibi olmaları gerektiği vurgulanmıştır. Teletıbbın tıp eğitimi müfredatındaki yeri örnek çalışmalarla paylaşılmıştır. Bulgular: Sağlık hizmetleri sunumunda teknolojik olanakların kullanımı ve uzaktan sağlık hizmeti uygulamaları gelişmekte ve yaygınlaşmaktadır. Avantajları ve kısıtlılıkları ile teletıp bu gelişmelerin öncüsüdür. Tanı ve tedavi işlemlerinin yanında eğitim, danışmanlık, yönderlik; hatta uzaktan cerrahi girişimlerin yönetimi mümkündür. Öğrenci memnuniyetinin yanı sıra hastalar için de yaralıdır. Sonuç: Tıp öğrencilerinin ve hekimlerin bu uygulamalar konusunda etkin bir kullanıcı olması, kendi personelleri ve hastalarının eğitimi konusunda önemli rol oynaması gerekmektedir. Zorunlu hallerde önemli hale gelen bu hizmet sunumu biçimi için öğrencilerin yüz yüze hasta yönetimi konusunda iyi yetişmiş olması gereklidir. Alan yazındaki örnek uygulamalar teletıp uygulamalarını ulusal ve uluslararası yeterlilik çerçevelerine uyarlamaya odaklanmıştır. Teletıbbın kullanıldığı durumlar, kısıtlılıkları, yasal ve etik boyutları müfredat içeresinde yer almalı, yeterlilik çerçevelerine uyarlanmalıdır.