Aims: Medical case vignettes play a crucial role in medical education, yet they often fail to authentically represent diverse patients. Moreover, these vignettes tend to oversimplify the complex relationship between patient characteristics and medical conditions, leading to biased and potentially harmful perspectives among students. Displaying aspects of patient diversity, such as ethnicity, in written cases proves challenging. Additionally, creating these cases places a significant burden on teachers in terms of labour and time. Our objective is to explore the potential of artificial intelligence (AI)‐assisted computer‐generated clinical cases to expedite case creation and enhance diversity, along with AI‐generated patient photographs for more lifelike portrayal.Methods: In this study, we employed ChatGPT (OpenAI, GPT 3.5) to develop diverse and inclusive medical case vignettes. We evaluated various approaches and identified a set of eight consecutive prompts that can be readily customized to accommodate local contexts and specific assignments. To enhance visual representation, we utilized Adobe Firefly beta for image generation.Results: Using the described prompts, we consistently generated cases for various assignments, producing sets of 30 cases at a time. We ensured the inclusion of mandatory checks and formatting, completing the process within approximately 60 min per set.Conclusions: Our approach significantly accelerated case creation and improved diversity, although prioritizing maximum diversity compromised representativeness to some extent. While the optimized prompts are easily reusable, the process itself demands computer skills not all educators possess. To address this, we aim to share all created patients as open educational resources, empowering educators to create cases independently.

Aims: In medical education, the ability of large language models (LLMs) to match human performance raises questions about their potential as educational tools. This study evaluates LLMs' performance on Clinical Pharmacology and Therapeutics (CPT) exams, comparing their results to medical students and exploring their ability to identify poorly formulated multiple‐choice questions (MCQs).Methods: ChatGPT‐4 Omni, Gemini Advanced, Le Chat and DeepSeek R1 were tested on local CPT exams (third year of bachelor's degree, first/second year of master's degree) and the European Prescribing Exam (EuroPE⁺). The exams included MCQs and open‐ended questions assessing knowledge and prescribing skills. LLM results were analysed using the same scoring system as students. A confusion matrix was used to evaluate the ability of ChatGPT and Gemini to identify ambiguous/erroneous MCQs.Results: LLMs achieved comparable or superior results to medical students across all levels. For local exams, LLMs outperformed M1 students and matched L3 and M2 students. In EuroPE⁺, LLMs significantly outperformed students in both the knowledge and prescribing skills sections. All LLM errors in EuroPE⁺ were genuine (100%), whereas local exam errors were frequently due to ambiguities or correction flaws (24.3%). When both ChatGPT and Gemini provided the same incorrect answer to an MCQ, the specificity for detecting ambiguous questions was 92.9%, with a negative predictive value of 85.5%.Conclusion: LLMs demonstrate capabilities comparable to or exceeding medical students in CPT exams. Their ability to flag potentially flawed MCQs highlights their value not only as educational tools but also as quality control instruments in exam preparation.

Purpose: To harmonize and modernize clinical pharmacology and therapeutics (CPT) education across Europe, we developed the European Prescribing Exam. Before its introduction into medical degree programs, it is crucial to understand the potential barriers to its implementation and ways to overcome them. Therefore, the aim of this study was to identify barriers and potential solutions to the implementation of the European Prescribing Exam. Methods: This qualitative World Café (WC) study involved CPT teachers who participated in a 2-day event focused on the European Prescribing Exam. There were five tables in the WC, each dedicated to a different topic of implementation: (1) organization, (2) technical aspects, (3) content, (4) rollout logistics, and (5) politics. Participants rotated randomly between the tables every 20 min. During each round, they were encouraged to identify barriers and solutions, which were then discussed. The rounds continued until data saturation was reached. Findings were summarized at the end of the WC. We used inductive thematic analysis using a semantic approach to analyze the data. Results: In total, 26 CPT teachers (female: n = 14) from 19 medical schools in 15 European countries participated. After four rounds, 86 potential barriers and 86 solutions were identified. Most barriers were related to the topics “Content” (n = 22), “Organization” (n = 20), and “Technical aspects” (n = 18). Thematic analysis identified 11 themes, three of which were overarching, meaning they applied to multiple topics. The most significant themes included barriers related to curricula, motivation, information technology, and relevance. Conclusion: This study shows that organizing and implementing the European Prescribing Exam will be challenging. However, participants proposed potential solutions for nearly all barriers, which suggest that the implementation of the European Prescribing Exam is feasible.

In the light of the rapidly increasing global incidence of, and therapeutic arsenal for, diabetes type 2, this brief report underscores the need for advancements in clinical pharmacology and therapeutics (CPT) education with regard to diabetes type 2. We advocate for the comprehensive training of medical students and junior doctors in line with current guidelines, and emphasize the importance of teaching how to draw up individualized treatment plans based on patients’ specific risk factors and conditions, such as cardiovascular risks, weight, and risk of hypoglycaemia. Within the curriculum, traditional teaching approaches should be replaced by innovative methods such as problem-based learning, which has been shown to be more effective in developing prescribing knowledge and skills. The inclusion of real-world experience and interprofessional learning via so-called student-run clinics is also recommended. Subsequently, innovative assessment methods like the European Prescribing Exam and objective structured clinical examinations (OSCE) are highlighted as essential for evaluating knowledge and practical skills. By adopting these educational advances, medical education can better equip future practitioners to adequately manage the complex pharmacological treatment of diabetes.