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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. [ABSTRACT FROM AUTHOR]

Introduction: Therapeutic decision-making often involves weighing several treatment options on the basis of, among other things, the disease profile, patient characteristics but also prescriber-specific factors. This study investigated the factors influencing therapeutic decision-making among prescribers and explores how these factors differ between healthcare professionals., Methods: This Q-methodology study involved 37 participants from different medical backgrounds, including physicians, dentists, midwives, physician assistants and advanced nurse practitioners from various intra- and extramural settings in the Netherlands. Participants ranked 55 factors on their importance for medication prescribing, and factor analysis was used to identify distinct prescriber profiles. These profiles were subsequently enriched using qualitative data derived from post-sorting interviews., Results: Four prescriber profiles were identified that explained 59% of the study variance: pragmatic contextual, guideline oriented, experience driven and vulnerability focused. Several factors were ranked differently in each profile. The pragmatic contextual profile emphasized patient context and risk prevention, the guideline-oriented profile adhered to clinical guidelines, the experience-driven profile relied on clinical experience and patient-centred communication, and the vulnerability-focused profile prioritized personalized care for specific patient groups., Conclusion: This study sheds light on the diverse factors influencing prescriber decision-making in medical practice. By identifying four prescriber profiles, it reveals the complexity and diversity in how prescribers balance guidelines, clinical experience and patient-specific considerations in their practice. These findings offer direction for developing educational and policy initiatives to enhance the quality of prescribing, ultimately improving patient outcomes., (© 2025 The Author(s). British Journal of Clinical Pharmacology published by John Wiley & Sons Ltd on behalf of British Pharmacological Society.)