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.

An accurate estimated glomerular filtration rate (eGFR) is essential in drug dosing. This study demonstrates the limitations of indexed (ml/min/1.73 m²) and de‐indexed (ml/min) eGFR based drug dosing in patients with obesity or underweight. This systematic study aimed to determine the most appropriate approach to estimate the GFR for standardized eGFR based drug dosing in these patients. (Raw) data of 12 studies were selected to investigate the accuracy and bias of both the indexed and de‐indexed estimations of the Modification of Diet in Renal Disease (MDRD) study equation and the Chronic Kidney Disease Epidemiology Collaboration equation (CKD‐EPI), and of the Cockcroft–Gault (CG) in patients with obesity or underweight. Accuracy was calculated as the proportion of eGFR values within 30% of the measured GFR (P30) using an inert tracer (e.g., iohexol, inulin, ⁵¹Cr‐EDTA, or iothalamate clearance). An accuracy of at least 80% was considered acceptable. GFR values estimated with the CG, MDRD, and CKD‐EPI differ significantly within a patient with obesity or underweight regardless of whether it is indexed or de‐indexed. All studies, with two exceptions, show that all three equations are inaccurate for patients with underweight or class II obesity (P30: 55%–94%). De‐indexing eGFR improves not or modestly the accuracy, and mostly remains below the 80% (P30: 62%–100%). CG was highly inaccurate in obese and underweight patients (P30: 7%–82%). Although these results show that CG is obsolete, the accuracy of MDRD and CKD‐EPI is low in patients with obesity or underweight and de‐indexing is not the solution. Better education and more accurate methods for appropriate drug dosing (e.g., measured GFR with inert tracer, therapeutic drug monitoring, or 24‐h creatinine clearance) are recommended.

Aim: The aim of this study was to investigate how the prescribing knowledge and skills of junior doctors in the Netherlands and Belgium develop in the year after graduation. We also analysed differences in knowledge and skills between surgical and nonsurgical junior doctors.Methods: This international, multicentre (n = 11), longitudinal study analysed the learning curves of junior doctors working in various specialties via three validated assessments at about the time of graduation, and 6 months and 1 year after graduation. Each assessment contained 35 multiple choice questions (MCQs) on medication safety (passing grade ≥85%) and three clinical scenarios.Results: In total, 556 junior doctors participated, 326 (58.6%) of whom completed the MCQs and 325 (58.5%) the clinical case scenarios of all three assessments. Mean prescribing knowledge was stable in the year after graduation, with 69% (SD 13) correctly answering questions at assessment 1 and 71% (SD 14) at assessment 3, whereas prescribing skills decreased: 63% of treatment plans were considered adequate at assessment 1 but only 40% at assessment 3 (P < .001). While nonsurgical doctors had similar learning curves for knowledge and skills as surgical doctors (P = .53 and P = .56 respectively), their overall level was higher at all three assessments (all P < .05).Conclusion: These results show that junior doctors' prescribing knowledge and skills did not improve while they were working in clinical practice. Moreover, their level was under the predefined passing grade. As this might adversely affect patient safety, educational interventions should be introduced to improve the prescribing competence of junior doctors.

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.

Rational prescribing is essential for the quality of health care. However, many final‐year medical students and junior doctors lack prescribing competence to perform this task. The availability of a list of medicines that a junior doctor working in Europe should be able to independently prescribe safely and effectively without supervision could support and harmonize teaching and training in clinical pharmacology and therapeutics (CPT) in Europe. Therefore, our aim was to achieve consensus on such a list of medicines that are widely accessible in Europe. For this, we used a modified Delphi study method consisting of three parts. In part one, we created an initial list based on a literature search. In part two, a group of 64 coordinators in CPT education, selected via the Network of Teachers in Pharmacotherapy of the European Association for Clinical Pharmacology and Therapeutics, evaluated the accessibility of each medicine in his or her country, and provided a diverse group of experts willing to participate in the Delphi part. In part three, 463 experts from 24 European countries were invited to participate in a 2‐round Delphi study. In total, 187 experts (40%) from 24 countries completed both rounds and evaluated 416 medicines, 98 of which were included in the final list. The top three Anatomical Therapeutic Chemical code groups were (1) cardiovascular system (n = 23), (2) anti‐infective (n = 21), and (3) musculoskeletal system (n = 11). This European List of Key Medicines for Medical Education could be a starting point for country‐specific lists and could be used for the training and assessment of CPT.